Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models

Description

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

Links & Downloads

1. https://digital.library.unt.edu/ark:/67531/metadc407796/
2. ark: ark:/67531/metadc407796

Tags

Maximum likelihood

estimation

logistic regression

sinusoidal regression

Additional Fields

Identifer	oai:union.ndltd.org:unt.edu/info:ark/67531/metadc407796
Date	12 1900
Creators	Weng, Yu
Contributors	Song, Kai-Sheng, Allaart, Pieter C., Wang, Jiaping
Publisher	University of North Texas
Source Sets	University of North Texas
Language	English
Detected Language	English
Type	Thesis or Dissertation
Format	Text
Rights	Public, Weng, Yu, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.