Stochastic Distance Between Burkitt Lymphoma/Leukemia Strains

Jesús E. García (), R. Gholizadeh and V. A. González López ()
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Jesús E. García: University of Campinas, Department of Statistics
R. Gholizadeh: University of Campinas
V. A. González López: University of Campinas, Department of Statistics

Chapter Chapter 13 in Demography and Health Issues, 2018, pp 143-153 from Springer

Abstract: Abstract Quantifying the proximity between N-grams allows to establish criteria of comparison between them. Recently, a consistent distance d to achieve this end was proposed, see García JE, González-López VA. Detecting regime changes in Markov models. In New trends in stochastic modeling and data analysis (chapter 2, page 103), 2015. This distance takes advantage of a model structure on Markovian processes in finite alphabets and with finite memories, called Partition Markov Models, see García JE, González-López VA. Entropy 19:160, 2017. In this work we explore the performance of d in a real problem, using d to establish a notion of natural proximity between DNA sequences from patients with identical diagnosis, which is: Burkitt lymphoma/leukemia. And we present a robust strategy of estimation to identify the stochastic law that governs most of the sequences considered, thus mapping out a common profile to all these patients, via their DNA sequences.

Keywords: Partition Markov models; Bayesian information criterion; Robust estimation in stochastic processes (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-319-76002-5_13

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DOI: 10.1007/978-3-319-76002-5_13

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