Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models

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We consider the problem of maximum likelihood estimation of logistic sinusoidal regression models and develop some asymptotic theory including the consistency and joint rates of convergence for the maximum likelihood estimators. The key techniques build upon a synthesis of the results of Walker and Song and Li for the widely studied sinusoidal regression model and on making a connection to a result of Radchenko. Monte Carlo simulations are also presented to demonstrate the finite-sample performance of the estimators

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Weng, Yu December 2013.

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This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 162 times , with 6 in the last month . More information about this dissertation can be viewed below.

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  • Weng, Yu

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We consider the problem of maximum likelihood estimation of logistic sinusoidal regression models and develop some asymptotic theory including the consistency and joint rates of convergence for the maximum likelihood estimators. The key techniques build upon a synthesis of the results of Walker and Song and Li for the widely studied sinusoidal regression model and on making a connection to a result of Radchenko. Monte Carlo simulations are also presented to demonstrate the finite-sample performance of the estimators

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  • December 2013

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  • Nov. 8, 2014, 11:56 a.m.

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  • Nov. 15, 2016, 10:28 p.m.

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Weng, Yu. Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models, dissertation, December 2013; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc407796/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .