On longest consecutive patterns in Markov chains

dc.contributor.advisorErnst, Philip A.en_US
dc.creatorXia, Yizhouen_US
dc.date.accessioned2019-11-12T13:49:31Zen_US
dc.date.available2020-12-01T06:01:10Zen_US
dc.date.created2019-12en_US
dc.date.issued2019-11-11en_US
dc.date.submittedDecember 2019en_US
dc.date.updated2019-11-12T13:49:33Zen_US
dc.description.abstractThe length of longest consecutive head in Bernoulli trials L(n) has been studied extensively and has been found applications in biology, finance and non-parametric statistics. The study of longest consecutive successes in random trials dates the work of de Moivre. Limiting theorems and large deviation results are provided for L(n) with the assumption of existence of stationary distribution. Given a discrete-time homogeneous Markov chain with initial state i, one extension from previous Bernoulli case is to study the distribution of L(j,n), the length of the longest consecutive visits of this chain to state j until time n. Our work focuses on studying L(j,n) for both homogeneous and time-nonhomogeneous Markov chains. In the existing literature, no limiting theorems of L(j,n) are derived under the case of time nonhomogeneous Markov chains. We are able to solve this by first deriving a new exact formula of the distribution of L(j,n) and then derive an upper and lower bound of P(L(j,n)<k) without the assumption of the existence of stationary distribution. Then we offer one convergence in probability theorem and one convergence almost surely theorem of L(j,n). We also offer a limiting result respect to the expectation of L(j,n). We also close an open problem about the large deviation results of L(j,n). We first establish asymptotics for the moment generating function of L(j,n) in general Markov chains without the assumption of the existence of stationary distribution and then we provide two large deviation principles of L(j,n). The existing large deviation results only consider Bernoulli trials and two state homogeneous Markov chains. Last, we provide two extensions. One is to study the length of longest consecutive visit of a pattern in Markov chains and the other one is to study the length of longest consecutive visit of a set of states in Markov chains. We consider Matrix to represent probabilities and theorems of primitive matrix help us to prove limiting theorems. Simulation results are also included.en_US
dc.embargo.terms2020-12-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationXia, Yizhou. "On longest consecutive patterns in Markov chains." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/107670">https://hdl.handle.net/1911/107670</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/107670en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectMarkov chainen_US
dc.subjectDiscrete stochastic processen_US
dc.subjectLarge deviation principleen_US
dc.subjectLaw of large numberen_US
dc.titleOn longest consecutive patterns in Markov chainsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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