Strong Consistency of Maximum Likelihood Estimators in Compound-Sequential Logit Model

Chang Ming Yin; Fang Fang Li; Lian Ju Su

doi:10.4028/www.scientific.net/AMM.742.429

Paper Titles

Exponential Stability for Stochastic Neural Networks with Time-Varying Delay and Leakage Delay
p.399

Simulation of Traffic Flow in Hurricane Emergency Evacuation Use Dynamic Control Strategy
p.404

A Time Series Analysis and Neural Network Based Scheme for Fault Diagnosis of Transformers
p.412

The Proof of Strong Markov Property Based on one Definition
p.419

Strong Consistency of Maximum Likelihood Estimators in Compound-Sequential Logit Model
p.429

Sparse L_p-Norm Based ICP for 3D Registration
p.433

A Practical Approach for Digital Forensic Triage
p.437

Strong Consistency of Maximum Likelihood Estimators in Sequential-Cumulative Logit Model
p.445

On the Almost sure Convergence Rate for Weighted Sums of Negatively Associated Random Variables with Application to Priestley-Chao Estimate
p.449

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 742Strong Consistency of Maximum Likelihood...

Strong Consistency of Maximum Likelihood Estimators in Compound-Sequential Logit Model

Abstract:

Compound-sequential logit models are extensions of the ordinary logistic regression model, which are designed for complex ordinal outcomes commonly seen in practice. In this paper, we prove strong consistency of the maximum likelihood estimator (MLE) of the regression parameter vector under some mild conditions. We relax the boundedness condition of the regressors required in most existing theoretical results, and all conditions are easy to verify.