Browsing by Author "Scott, David W."

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    A joint modeling approach for longitudinal microbiome data with time-to-event outcomes
    (2019-04-15) Luna, Pamela Nicole; Scott, David W.; Shaw, Chad A.
    Humans are living, breathing ecosystems. We share our bodies with a vast collection of microorganisms that easily outnumber the human cells in our bodies. While these microbes are mostly benign or beneficial, non-pathogenic and pathogenic microorganisms alike can be harmful in certain abundances. Microbial imbalances within anatomic sites have been linked to human illness, but the underlying mechanics of how individuals reach this state of dysbiosis are not well understood. Determining how changes in microbial abundances affect the onset of disease could lead to novel treatments that help to stabilize the microbiome. However, currently no methods appropriately quantify the associations between longitudinal microbial abundance changes and time-to-event outcomes. This lack of methodology is partially due to the inability to include time-dependent biomarker data in an event model. Though the existing joint model for longitudinal and time-to-event data addresses this issue by incorporating a linear mixed effects model into the hazard function of the Cox proportional hazards model, it does not allow for proper modeling of non-Gaussian microbiome data. In this thesis we present a joint modeling approach which uses a negative binomial mixed effects model to determine the longitudinal values included in the hazard function. We discuss how our model parameterization generates interpretable results and accounts for the underlying structure of microbiome data while still respecting its inherent compositionality. We outline how to simulate longitudinal microbiome data and associated event times to create a joint model dataset and show that our model performs better than existing alternatives. Finally, we illustrate how this methodology could be used to improve clinical diagnostics and therapeutics.
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    A new method for robust nonparametric regression
    (1990) Wang, Ferdinand Tsihung; Scott, David W.
    Consider the problem of estimating the mean function underlying a set of noisy data. Least squares is appropriate if the error distribution of the noise is Gaussian, and if there is good reason to believe that the underlying function has some particular form. But what if the previous two assumptions fail to hold? In this regression setting, a robust method is one that is resistant against outliers, while a nonparametric method is one that allows the data to dictate the shape of the curve (rather than choosing the best parameters for a fit from a particular family). Although it is easy to find estimators that are either robust or nonparametric, the literature reveals very few that are both. In this thesis, a new method is proposed that uses the fact that the $L\sb1$ norm naturally leads to a robust estimator. In spite of the $L\sb1$ norm's reputation for being computationally intractable, it turns out that solving the least absolute deviations problem leads to a linear program with special structure. By utilizing this property, but over local neighborhoods, a method that is also nonparametric is obtained. Additionally, the new method generalizes naturally to higher dimensions; to date, the problem of smoothing in higher dimensions has met with little success. A proof of consistency is presented, and the results from simulated data are shown.
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    A test of mode existence with applications to multimodality
    (1993) Minnotte, Michael C.; Scott, David W.
    Modes, or local maxima, are often among the most interesting features of a probability density function. Given a set of data drawn from an unknown density, it is frequently desirable to know whether or not the density is multimodal, and various procedures have been suggested for investigating the question of multimodality in the context of hypothesis testing. Available tests, however, suffer from the encumbrance of testing the entire density at once, frequently through the use of nonparametric density estimates using a single bandwidth parameter. Such a procedure puts the investigator examining a density with several modes of varying sizes at a disadvantage. A new test is proposed involving testing the reality of individual observed modes, rather than directly testing the number of modes of the density as a whole. The test statistic used is a measure of the size of the mode, the absolute integrated difference between the estimated density and the same density with the mode in question excised at the level of the higher of its two surrounding antimodes. Samples are simulated from a conservative member of the composite null hypothesis to estimate p-values within a Monte Carlo setting. Such a test can be combined with the graphical notion of a "mode tree," in which estimated mode locations are plotted over a range of kernel bandwidths. In this way, one can obtain a procedure for examining, in an adaptive fashion, not only the reality of individual modes, but also the overall number of modes of the density. A proof of consistency of the test statistic is offered, simulation results are presented, and applications to real data are illustrated.
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    Adaptive kernel density estimation
    (1994) Sain, Stephan R.; Scott, David W.
    The need for improvements over the fixed kernel density estimator in certain situations has been discussed extensively in the literature, particularly in the application of density estimation to mode hunting. Problem densities often exhibit skewness or multimodality with differences in scale for each mode. By varying the bandwidth in some fashion, it is possible to achieve significant improvements over the fixed bandwidth approach. In general, variable bandwidth kernel density estimators can be divided into two categories: those that vary the bandwidth with the estimation point (balloon estimators) and those that vary the bandwidth with each data point (sample point estimators). For univariate balloon estimators, it can be shown that there exists a bandwidth in regions of f where f is convex (e.g. the tails) such that the bias is exactly zero. Such a bandwidth leads to a MSE = $O(n\sp{-1})$ for points in the appropriate regions. A global implementation strategy using a local cross-validation algorithm to estimate such bandwidths is developed. The theoretical behavior of the sample point estimator is difficult to examine as the form of the bandwidth function is unknown. An approximation based on binning the data is used to study the behavior of the MISE and the optimal bandwidth function. A practical data-based procedure for determining bandwidths for the sample point estimator is developed using a spline function to estimate the unknown bandwidth function. Finally, the multivariate problem is briefly addressed by examining the shape and size of the optimal bivariate kernels suggested by Terrell and Scott (1992). Extensions of the binning and spline estimation ideas are also discussed.
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    An Old Dog Learns New Tricks: Novel Applications of Kernel Density Estimators on Two Financial Datasets
    (2017-12-01) Ginley, Matthew Cline; Ensor, Katherine B.; Scott, David W.
    In our first application, we contribute two nonparametric simulation methods for analyzing Leveraged Exchange Traded Fund (LETF) return volatility and how this dynamic is related to the underlying index. LETFs are constructed to provide the indicated leverage multiple of the daily total return on an underlying index. LETFs may perform as expected on a daily basis; however, fund issuers state there is no guarantee of achieving the multiple of the index return over longer time horizons. Most, if not all LETF returns data are difficult to model because of the extreme volatility present and limited availability of data. First, to isolate the effects of daily, leveraged compounding on LETF volatility, we propose an innovative method for simulating daily index returns with a chosen constraint on the multi-day period return. By controlling for the performance of the underlying index, the range of volatilities observed in a simulated sample can be attributed to compounding with leverage and the presence of tracking errors. Second, to overcome the limited history of LETF returns data, we propose a method for simulating implied LETF tracking errors while still accounting for their dependence on underlying index returns. This allows for the incorporation of the complete history of index returns in an LETF returns model. Our nonparametric methods are flexible-- easily incorporating any chosen number of days, leverage ratios, or period return constraints, and can be used in combination or separately to model any quantity of interest derived from daily LETF returns. For our second application, we tackle binary classification problems with extremely low class 1 proportions. These ``rare events'' problems are a considerable challenge, which is magnified when dealing with large datasets. Having a minuscule count of class 1 observations motivates the implementation of more sophisticated methods to minimize forecasting bias towards the majority class. We propose an alternative approach to established up-sampling or down-sampling algorithms driven by kernel density estimators to transform the class labels to continuous targets. Having effectively transformed the problem from classification to regression, we argue that under the assumption of a monotonic relationship between predictors and the target, approximations of the majority class are possible in a rare events setting with the use of simple heuristics. By significantly reducing the burden posed by the majority class, the complexities of minority class membership can be modeled more effectively using monotonically constrained nonparametric regression methods. Our approach is demonstrated on a large financial dataset with an extremely low class 1 proportion. Additionally, novel features engineering is introduced to assist in the application of the density estimator used for class label transformation.
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    Application of Bayesian Modeling in High-throughput Genomic Data and Clinical Trial Design
    (2013-08-23) Xu, Yanxun; Cox, Dennis D.; Ji, Yuan; Qiu, Peng; Scott, David W.; Nakhleh, Luay K.
    My dissertation mainly focuses on developing Bayesian models for high-throughput data and clinical trial design. Next-generation sequencing (NGS) technology generates millions of short reads, which provide valuable information for various aspects of cellular activities and biological functions. So far, NGS techniques have been applied in quantitatively measurement of diverse platforms, such as RNA expression, DNA copy number variation (CNV) and DNA methylation. Although NGS is powerful and largely expedite biomedical research in various fields, challenge still remains due to the high modality of disparate high-throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics, e.g., how to extract useful information for the enormous data produced by NGS or how to effectively integrate the information from different platforms. Bayesian has the potential to fill in these gaps. In my dissertation, I will propose Bayesian-based approaches to address above challenges so that we can take full advantage of the NGS technology. It includes three specific topics: (1) proposing BM-Map: a Bayesian mapping of multireads for NGS data, (2) proposing a Bayesian graphical model for integrative analysis of TCGA data, and (3) proposing a non- parametric Bayesian Bi-clustering for next generation sequencing count data. For the clinical trial design, I will propose a latent Gaussian process model with application to monitoring clinical trials.
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    Bayesian decision-theoretic method and semi-parametric approach with applications in clinical trial designs and longitudinal studies
    (2013-11-25) Jiang, Fei; Lee, J. Jack; Cox, Dennis D.; Scott, David W.; Ma, Yanyuan; Tapia, Richard A.
    The gold of biostatistical researches is to develop statistical tools that improves human health or increases understanding of human biology. One area of the studies focuses on designing clinical trials to find out if new drugs or treatments are efficacious. The other area focuses on studying diseases related variables, which gives better understanding of the diseases. The thesis explores these areas from both theoretical and practical points of views. In addition, the thesis develop statistical devices which improve the existing methods in these areas. Firstly, the thesis proposes a Bayesian decision-theoretic group sequential – adaptive randomization phase II clinical trial design. The design improves the trial efficiency by increasing statistical power and reducing required sample sizes. The design also increases patients’ individual benefit, because it enhances patients’ opportunities of receiving better treatments. Secondly, the thesis develops a semiparametric restricted moment model and a score imputation estimation for survival analysis. The method is more robust than the parametric alternatives. In addition to data analysis, the method is used to design a seamless phase II/III clinical trial. The seamless phase II/III clinical trial design shortens the durations between phase II and III studies, and improves the efficiency of the traditional designs by utilizing additional short term information for making decisions. Finally, the thesis develops a partial linear time varying semi-parametric single-index risk score model and a fused B-spline/kernel estimation for longitudinal data analysis. The method models confounder effects linearly. In addition, it uses a nonparametric nonlinear function, namely the single-index risk score, to model the effects of interests. The fused B-spline/kernel technique estimates both the parametric and nonparametric components consistently. The methodology is applied to study the onsite of Huntington’s disease in determining certain time varying covariate effects on the disease risk.
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    Benefit-cost analysis in rehabilitation programs
    (1980) Chan, Shou Alice; Thrall, Robert M.; Scott, David W.; Cardus, David
    This thesis is concerned with an evaluation process for selection of rehabilitation research projects. An existing several-variable model is studied and extended. The parts of this model that relate to construction of a utility function are discussed in detail. A number of examples are given for illustration.
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    Biased and Unbiased Cross-Validation in Density Estimation
    (1987-02) Scott, David W.; Terrell, George R.
    Non parametric density estimation requires the specification of smoothing parameters. The demand of statistical objectivity make it highly desirable to base the choice on properties of the data set. In this paper we introduce some biased cross-validation criteria for selection of smoothing parameters for kernel and histogram density estimators, closely related to one investigated in Scott and Factor (1981). These criteria are obtained by estimating L2-norms of derivatives of the unknown density and provide slightly biased estimates of the average squared-L2 error or mean integrated squared error. These criteria are roughly the analog of Wahba's (1981) generalized cross-validation procedure for orthogonal series density estimators. We present the relationship of the biased cross-validation procedure to the least squares cross-validation procedure, which provides unbiased estimates of the mean integrated squared error. Both methods are shown to be based on U-statistics. We compare the two methods by theoretical calculation of the noise in the cross-validation functions and corresponding cross-validated smoothing parameters, by Monte Carlo simulation, and by example. Surprisingly large gains in asymptotic efficiency are observed when biased cross-validation is compared to unbiased cross-validation if the underlying density is sufficiently smooth. The theoretical results explain some of the small sample behavior of cross-validation functions: we show that cross-validation algorithms can be unreliable for samples sizes which are "too small." In order to aid the practitioner in the use of these appealing automatic cross-validation algorithms and to help facilitate evaluation of future algorithms, we must address some ofttimes controversial issues in density estimation: squared loss, the integrate squared error and mean integrated squared error criteria, adaptive density estimates, sample size requirements, and assumptions about the underlying density's smoothness. We conclude that the two cross-validation procedures behave quite differently so that one might well use both in practice.
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    Biodegradable, phosphate-containing, dual-gelling macromers for cellular delivery in bone tissue engineering
    (Elsevier, 2015) Watson, Brendan M.; Vo, Tiffany N.; Tatara, Alexander M.; Shah, Sarita R.; Scott, David W.; Engel, Paul S.; Mikos, Antonios G.; Bioengineering; Chemistry; Statistics
    Injectable, biodegradable, dual-gelling macromer solutions were used to encapsulate mesenchymal stem cells (MSCs) within stable hydrogels when elevated to physiologic temperature. Pendant phosphate groups were incorporated in the N-isopropyl acrylamide-based macromers to improve biointegration and facilitate hydrogel degradation. The MSCs were shown to survive the encapsulation process, and live cells were detected within the hydrogels for up to 28 days inﾠvitro. Cell-laden hydrogels were shown to undergo significant mineralization in osteogenic medium. Cell-laden and acellular hydrogels were implanted into a critical-size rat cranial defect for 4 and 12 weeks. Both cell-laden and acellular hydrogels were shown to degrade inﾠvivo and help to facilitate bone growth into the defect. Improved bone bridging of the defect was seen with the incorporation of cells, as well as with higher phosphate content of the macromer. Furthermore, direct bone-to-hydrogel contact was observed in the majority of implants, which is not commonly seen in this model. The ability of these macromers to deliver stem cells while forming in situ and subsequently degrade while facilitating bone ingrowth into the defect makes this class of macromers a promising material for craniofacial bone tissue engineering.
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    Comparison of data-based methods for non-parametric density estimation
    (1979) Factor, Lynette Ethel; Thompson, James R.; Scott, David W.; Gorry, G. Anthony
    There have been recent developments in data-based methods for estimating densities non-parametrically. In this work we shall compare some methods developed by Scott, Duin and Wahba according to their sensitivity, statistical accuracy and cost of implementation when applied to one-dimensional data sets. We shall illustrate the limitations and tradeoffs of each method. The estimates obtained by each method will also be compared to the maximum likelihood univariate Gaussian estimate. We shall also illustrate the application of Duin's method to two-dimensional data sets and compare the results to the maximum likelihood bivariate Gaussian estimate.
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    Correlation of nuclear pIGF-1R/IGF-1R and YAP/TAZ in a tissue microarray with outcomes in osteosarcoma patients
    (Oncotarget, 2022) Molina, Eric R.; Chim, Letitia K.; Lamhamedi-Cherradi, Salah-Eddine; Mohiuddin, Sana; McCall, David; Cuglievan, Branko; Krishnan, Sandhya; Porter, Robert W.; Ingram, Davis R.; Wang, Wei-Lien; Lazar, Alexander J.; Scott, David W.; Truong, Danh D.; Daw, Najat C.; Ludwig, Joseph A.; Mikos, Antonios G.; Bioengineering; Statistics
    Osteosarcoma (OS) is a genetically diverse bone cancer that lacks a consistent targetable mutation. Recent studies suggest the IGF/PI3K/mTOR pathway and YAP/TAZ paralogs regulate cell fate and proliferation in response to biomechanical cues within the tumor microenvironment. How this occurs and their implication upon osteosarcoma survival, remains poorly understood. Here, we show that IGF-1R can translocate into the nucleus, where it may act as part of a transcription factor complex. To explore the relationship between YAP/TAZ and total and nuclear phosphorylated IGF-1R (pIGF-1R), we evaluated sequential tumor sections from a 37-patient tissue microarray by confocal microscopy. Next, we examined the relationship between stained markers, clinical disease characteristics, and patient outcomes. The nuclear to cytoplasmic ratios (N:C ratio) of YAP and TAZ strongly correlated with nuclear pIGF-1R (r = 0.522, p = 0.001 for each pair). Kaplan–Meier analyses indicated that nuclear pIGF-1R predicted poor overall survival, a finding confirmed in the Cox proportional hazards model. Though additional investigation in a larger prospective study will be required to validate the prognostic accuracy of these markers, our results may have broad implications for the new class of YAP, TAZ, AXL, or TEAD inhibitors that have reached early phase clinical trials this year.
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    Denoising by wavelet thresholding using multivariate minimum distance partial density estimation
    (2006) Scott, Alena I.; Scott, David W.
    In this thesis, we consider wavelet-based denoising of signals and images contaminated with white Gaussian noise. Existing wavelet-based denoising methods are limited because they make at least one of the following three unrealistic assumptions: (1) the wavelet coefficients are independent, (2) the signal component of the wavelet coefficient distribution follows a specified parametric model, and (3) the wavelet representations of all signals of interest have the same level of sparsity. We develop an adaptive wavelet thresholding algorithm that addresses each of these issues. We model the wavelet coefficients with a two-component mixture in which the noise component is Gaussian but the signal component need not be specified. We use a new technique in density estimation which minimizes an distance criterion (L2E) to estimate the parameters of the partial density that represents the noise component. The L2E estimate for the weight of the noise component, w&d4;L2E , determines the fraction of wavelet coefficients that the algorithm considers noise; we show that w&d4;L2E corresponds to the level of complexity of the signal. We also incorporate information on inter-scale dependencies by modeling across-scale (parent/child) groups of adjacent coefficients with multivariate densities estimated by L 2E. To assess the performance of our method, we compare it to several standard wavelet-based denoising algorithms on a number of benchmark signals and images. We find that our method incorporating inter-scale dependencies gives results that are an improvement over most of the standard methods and are comparable to the rest. The L2E thresholding algorithm performed very well for 1-D signals, especially those with a considerable amount of high frequency content. Our method worked reasonably well for images, with some apparent advantage in denoising smaller images. In addition to providing a standalone denoising method, L2E can be used to estimate the variance of the noise in the signal for use in other thresholding methods. We also find that the L2E estimate for the noise variance is always comparable and sometimes better than the conventional median absolute deviation estimator.
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    Denoising Non-stationary Signals by Dynamic Multivariate Complex Wavelet Thresholding
    (SSRN, 2020) Raath, Kim; Ensor, Katherine B.; Scott, David W.; Crivello, Alena
    Over the past few years, we have seen an increased need for analyzing the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transforms (DWT), a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically-optimized, multivariate thresholding method. Applying this method we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal rich time series, typically observed in finance. Supplementary materials for your article are available online.
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    Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding
    (MDPI, 2023) Raath, Kim C.; Ensor, Katherine B.; Crivello, Alena; Scott, David W.
    Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.
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    Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding
    (MDPI, 2023) Raath, Kim C.; Ensor, Katherine B.; Crivello, Alena; Scott, David W.
    Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (𝑊𝑎𝑣𝑒𝐿2𝐸). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.
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    Essays in Efficiency Analysis
    (2013-09-16) Demchuk, Pavlo; Sickles, Robin C.; Hartley, Peter R.; Scott, David W.
    Today a standard procedure to analyze the impact of environmental factors on productive efficiency of a decision making unit is to use a two stage approach, where first one estimates the efficiency and then uses regression techniques to explain the variation of efficiency between different units. It is argued that the abovementioned method may produce doubtful results which may distort the truth data represents. In order to introduce economic intuition and to mitigate the problem of omitted variables we introduce the matching procedure which is to be used before the efficiency analysis. We believe that by having comparable decision making units we implicitly control for the environmental factors at the same time cleaning the sample of outliers. The main goal of the first part of the thesis is to compare a procedure including matching prior to efficiency analysis with straightforward two stage procedure without matching as well as an alternative of conditional efficiency frontier. We conduct our study using a Monte Carlo study with different model specifications and despite the reduced sample which may create some complications in the computational stage we strongly agree with a notion of economic meaningfulness of the newly obtained results. We also compare the results obtained by the new method with ones previously produced by Demchuk and Zelenyuk (2009) who compare efficiencies of Ukrainian regions and find some differences between the two approaches. Second part deals with an empirical study of electricity generating power plants before and after market reform in Texas. We compare private, public and municipal power generators using the method introduced in part one. We find that municipal power plants operate mostly inefficiently, while private and public are very close in their production patterns. The new method allows us to compare decision making units from different groups, which may have different objective schemes and productive incentives. Despite the fact that at a certain point after the reform private generators opted not to provide their data to the regulator we were able to construct tree different data samples comprising two and three groups of generators and analyze their production/efficiency patterns. In the third chapter we propose a semiparametric approach with shape constrains which is consistent with monotonicity and concavity constraints. Penalized splines are used to maintain the shape constrained via nonlinear transformations of spline basis expansions. The large sample properties, an effective algorithm and method of smoothing parameter selection are presented in the paper. Monte Carlo simulations and empirical examples demonstrate the finite sample performance and the usefulness of the proposed method.
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    Essays on Healthcare Access, Use, and Cost Containment
    (2012-09-05) Dugan, Jerome; Ho, Vivian; Boylan, Richard T.; Scott, David W.
    This dissertation is composed of two essays that examine the role of public and private health insurance on healthcare access, use, and cost containment. In Chapter 1, Dugan, Virani, and Ho examine the impact of Medicare eligibility on healthcare utilization and access. Although Medicare eligibility has been shown to generally increase health care utilization, few studies have examined these relationships among the chronically ill. We use a regression-discontinuity framework to compare physician utilization and financial access to care among people before and after the Medicare eligibility threshold at age 65. Specifically, we focus on coronary heart disease and stroke (CHDS) patients. We find that Medicare eligibility improves health care access and physician utilization for many adults with CHDS, but it may not promote appropriate levels of physician use among blacks with CHDS. My second chapter examines the extent to which the managed care backlash affected managed care's ability to contain hospital costs among short-term, non-federal hospitals between 1998 and 2008. My analysis focuses on health maintenance organizations (HMOs), the most aggressive managed care model. Unlike previous studies that use cross-sectional or fixed effects estimators to address the endogeneity of HMO penetration with respect to hospital costs, this study uses a fixed effects instrumental variable approach. The results suggest two conclusions. First, I find the impact of increased HMO penetration on costs declined over the study period, suggesting regulation adversely impacted managed care's ability to contain hospital costs. Second, when costs are decomposed into unit costs by hospital service, I find the impact of increased HMO penetration on inpatient costs reversed over the study period, but HMOs were still effective at containing outpatient costs.
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    Essays on Productivity and Panel Data Econometrics
    (2014-03-24) Liu, Junrong; Sickles, Robin C.; Sizova, Natalia M.; Scott, David W.
    There are four essays on productivity and Panel data econometrics in this dissertation, with the first two essays on empirical research and the last two more focused on theory improvement. The first chapter is study of productivity and efficiency in the Mexican Energy Industry. The second chapter analyzes the productivity and efficiency of U.S. largest banks productivity and efficiency. The third incorporates a Bayesian treatment to two different panel data models. The last chapter introduces a semi-nonparametric method in panel data models. These four chapters have been developed into four working papers. They are Liu et al. (2011), Inanoglu et al. (2012), Liu et al. (2013) and Liu et al. (2014). The first chapter studies the optimizing behavior of Pemex by estimating a cost model of Pemex's production of energy. The estimation using duality between the cost and production function is undertaken, which facilitates the specification. This approach makes it convenient to find the cost shares under different levels of returns to scale. The results indicate the presence of substantial distortions in cost shares. That would be brought back to equilibrium were the Mexican government willing to allow more foreign investment in its energy extraction industry and thus increase the capital use and decrease the labor use. The second chapter utilizes a suite of panel data models in order to examine the extent to which scale economy and efficiencies exist in the largest U.S. banks. The empirical results are assessed based on the consensus among the findings from the various econometric treatments and models. This empirical study is based on a newly developed dataset based on Call Reports from the FDIC for the period 1994-2013. The analyses point to a number of conclusions. First, despite rapid growth over the last 20 years, the largest surviving banks in the U.S. have decreased their level of efficiency as they took on increasing levels of risk (credit, market and liquidity). Second, no measurable scale economies and scope economies are found across our host of models and econometric treatments. In addition to the broad policy implications, this essay also provides an array of econometric techniques, findings from which can be combined to provide a set of robust consensus-based conclusions that can be a valuable analytical tool for supervisors and others involved in the regulatory oversight of financial institutions. The third chapter considers two models for uncovering information about technical change in large heterogeneous panels. The first is a panel data model with nonparametric time effects. The second is a panel data model with common factors whose number is unknown and whose effects are firm-specific. This chapter proposes a Bayesian approach to estimate the two models. Bayesian inference techniques organized around MCMC are applied to implement the models. Monte Carlo experiments are performed to examine the finite-sample performance of this approach and have shown that the method proposed is comparable to the recently proposed estimator of Kneip et al. (2012) (KSS) and dominates a variety of estimators that rely on parametric assumptions. In order to illustrate the new method, the Bayesian approach has been applied to the analysis of efficiency trends in the U.S. largest banks using a dataset based on the Call Report data from FDIC over the period from 1990 to 2009. The fourth chapter introduces a new estimation method under the framework of the stochastic frontier production model. The noise term is assumed to have the traditional normal density but the inefficiency term is spanned by Laguerre Polynomials. This method is a Semi-nonparametric method and follows the spirit of Gallant and Nychka (1987). Finite sample performance of this estimator is shown to dominate the nonparametric estimators via Monte Carlo simulations.
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    Essays on Treatment Effects Evaluation
    (2012-09-05) Guo, Ronghua; Sickles, Robin C.; Sizova, Natalia M.; Scott, David W.
    The first chapter uses the propensity score matching method to measure the average impact of insurance on health service utilization in terms of office-based physician visits, total number of reported visits to hospital outpatient departments, and emergency room visits. Four matching algorithms are employed to match propensity scores. The results show that insurance significantly increases office-based physician visits, and its impacts on reported visits to hospital outpatient departments and emergency room visits are positive, but not significant. This implies that physician offices will receive a substantial increase in demand if universal insurance is imposed. Government will need to allocate more resources to physician offices relative to outpatient or emergency room services in the case of universal insurance in order to accommodate the increased demand. The second chapter studies the sensitivity of propensity score matching methods to different estimation methods. Traditionally, parametric models, such as logit and probit, are used to estimate propensity score. Current technology allows us to use computationally intensive methods, either semiparametric or nonparametric, to estimate it. We use the Monte Carlo experimental method to investigate the sensitivity of the treatment effect to different propensity score estimation models under the unconfoundedness assumption. The results show that the average treatment effect on the treated (ATT) estimates are insensitive to the estimation methods when index function for treatment is linear, but logit and probit model do better jobs when the index function is nonlinear. The third chapter proposes a Cross-Sectionally Varying (CVC) Coefficient method to approximate individual treatment effects with nonexperimental data, the distribution of treatment effects, the average treatment effect on the treated and the average treatment effect. The CVC method reparameterizes the outcome of no treatment and the treatment effect in terms of observable variables, and uses these observables together with a Bayesian estimator of their coefficients to approximate individual treatment effects. Monte Carlo simulations demonstrate the efficacy and applicability of the proposed estimator. This method is applied to two datasets: data from the U.S. Job Training Partnership ACT (JTPA) program and a dataset that contains firms’ seasoned equity offerings and operating performances.