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Large and moderate deviations in testing Rayleigh diffusion model

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This paper studies hypothesis testing in Rayleigh diffusion processes. With the help of large and moderate deviations for the log-likelihood ratio process, we give the negative regions and obtain the decay rates of the error probabilities.

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References

  • Bishwal JPN (2008a) Parameter estimation in stochastic differential equations. Lecture Notes in Mathematics, Vol 1923. Springer, Berlin

  • Bishwal JPN (2008b) Large deviations in testing fractional Ornstein-Uhlenbeck models. Stat Probab Lett 78: 953–962

    Article  MathSciNet  MATH  Google Scholar 

  • Blahut RE (1984) Hypothesis testing and information theory. IEEE Trans Inf Theory 20: 405–415

    Article  MathSciNet  Google Scholar 

  • Chiyonobu T (2003) Hypothesis testing for signal detection problem and large deviations. Nagoya Math J 162: 187–203

    MathSciNet  Google Scholar 

  • Dembo A, Zeitouni O (1998) Large deviations techniques and applications. Springer, New York

    Book  MATH  Google Scholar 

  • Gapeev PV, Küchler U (2008) On large deviations in testing Ornstein-Uhlenbeck-type models. Stat Inference Stoch Process 11: 143–155

    Article  MathSciNet  MATH  Google Scholar 

  • Giorno V, Nobile A, Ricciarde L, Sacerdote L (1986) Some remarks on the Rayleigh process. J Appl Probab 23: 398–408

    Article  MathSciNet  MATH  Google Scholar 

  • Gutiérrez R, Gutiérrez-Sánchez R, Nafidi A (2006) The statistic Rayleigh diffusion model: statistical inference and computational aspects. Applications to modelling of real cases. Appl Math Comput 175: 628–644

    Article  MathSciNet  MATH  Google Scholar 

  • Gutiérrez R, Gutiérrez-Sánchez R, Nafidi A (2008) Trend analysis and computational statistical estimation in a stochastic Rayleigh model: simulation and application. Math Comput Simul 77: 209–217

    Article  MATH  Google Scholar 

  • Han TS, Kobayashi K (1989) The strong converse theorem in hypothesis testing. IEEE Trans Inf Theory 35: 178–180

    Article  MathSciNet  MATH  Google Scholar 

  • Jiang H, Zhao SJ (2011) Large and moderate deviations in testing time inhomogeneous diffusions. J Stat Plan Inference 141: 3160–3169

    Article  MathSciNet  MATH  Google Scholar 

  • Seidel W, Mosler K, Alker M (2000) Likelihood ratio tests based on subglobal optimization: a power comparison in exponential mixture models. Stat Pap 41: 85–98

    Article  MATH  Google Scholar 

  • Shen JS, Liang W, He SY (2010) Likelihood ratio inference for mean residual life. Stat Pap Online 8

  • Torabi H, Behboodian J (2007) Likelihood ratio tests for fuzzy hypotheses testing. Stat Pap 48: 509–522

    Article  MATH  Google Scholar 

  • Zhao SJ, Gao FQ (2010) Large deviations in testing Jacobi model. Stat Probab Lett 80: 34–41

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Nenghui Kuang.

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Kuang, N., Xie, H. Large and moderate deviations in testing Rayleigh diffusion model. Stat Papers 54, 591–603 (2013). https://doi.org/10.1007/s00362-012-0450-5

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  • DOI: https://doi.org/10.1007/s00362-012-0450-5

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