Strong Consistency of Maximum Likelihood Estimators n Exponential Sequential Model

2014 ◽  
Vol 519-520 ◽  
pp. 878-882
Author(s):  
Chang Ming Yin ◽  
Bo Hong Chen ◽  
Shuang Hua Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Regression Parameter ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Sequential Model ◽  
Mild Conditions ◽  
Strongly Consistent

For the exponential sequential model, we show that maximum likelihood estimator of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

Strong Consistency of Maximum Likelihood Estimators in Extreme-Maximum-Value Distribution Model

2014 ◽  
Vol 525 ◽  
pp. 671-676
Author(s):  
Chang Ming Yin ◽  
Bo Hong Chen ◽  
Shuang Hua Liu
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Value Distribution ◽  
Maximum Likelihood Estimates ◽  
Distribution Model ◽  
Parameter Vector ◽  
Mild Conditions ◽  
Maximum Value ◽  
Strongly Consistent

For the extreme-maximum-value distribution model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

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

2015 ◽  
Vol 742 ◽  
pp. 445-448
Author(s):  
Chang Ming Yin ◽  
Xiao Jie Li ◽  
Dan Fu
Keyword(s):  
Maximum Likelihood ◽  
Logit Model ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Maximum Likelihood Estimates ◽  
Parameter Vector ◽  
Mild Conditions ◽  
Cumulative Logit Model ◽  
Cumulative Logit ◽  
Strongly Consistent

In this article, for the sequential-cumulative logit model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

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

2015 ◽  
Vol 742 ◽  
pp. 429-432
Author(s):  
Chang Ming Yin ◽  
Fang Fang Li ◽  
Lian Ju Su
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Logit Models ◽  
Parameter Vector ◽  
Boundedness Condition ◽  
Likelihood Estimator ◽  
Mild Conditions ◽  
Ordinary Logistic Regression ◽  
Theoretical Results

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.

On the maximum likelihood estimator for a discrete multivariate crash frequencies model

2020 ◽  
Vol 15 (2) ◽  
pp. 2335-2348
Author(s):  
Issa Cherif Geraldo
Keyword(s):  
Statistical Analysis ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Road Safety ◽  
Strong Consistency ◽  
Delta Method ◽  
Closed Form Expression ◽  
Parameter Vector ◽  
Form Expression ◽  
Likelihood Estimator

In this paper, we study the maximum likelihood estimator (MLE) of the parameter vector of a discrete multivariate crash frequencies model used in the statistical analysis of the effectiveness of a road safety measure. We derive the closed-form expression of the MLE afterwards we prove its strong consistency and we obtain the exact variance of the components of the MLE except one component whose variance is approximated via the delta method.

scholarly journals Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models

2017 ◽  
Vol 15 (1) ◽  
pp. 1539-1548
Author(s):  
Haiyan Xuan ◽  
Lixin Song ◽  
Muhammad Amin ◽  
Yongxia Shi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Garch Model ◽  
Garch Models ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Comparison Results ◽  
Quasi Maximum Likelihood ◽  
The Garch Model

Abstract This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals. The QMLE is proposed to the parameter vector of the GARCH model with the Laplace (1,1) firstly. Under some certain conditions, the strong consistency and asymptotic normality of QMLE are then established. In what follows, a real example with Laplace and normal distribution is analyzed to evaluate the performance of the QMLE and some comparison results on the performance are given. In the end the proofs of some theorem are presented.

scholarly journals On the Maximum Likelihood Estimation of Extreme Value Index Based on k-Record Values

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Abderrahim Louzaoui ◽  
Mohamed El Arrouchi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Likelihood Estimation ◽  
Record Values ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Likelihood Estimator ◽  
Consistent Solution ◽  
Strongly Consistent

In this paper, we study the existence and consistency of the maximum likelihood estimator of the extreme value index based on k-record values. Following the method used by Drees et al. (2004) and Zhou (2009), we prove that the likelihood equations, in terms of k-record values, eventually admit a strongly consistent solution without any restriction on the extreme value index, which is not the case in the aforementioned studies.

Design problems for the pure birth process

1983 ◽  
Vol 15 (2) ◽  
pp. 255-273 ◽  
Author(s):  
Gerhard Becker ◽  
Götz Kersting
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Maximum Likelihood Estimators ◽  
Optimal Designs ◽  
Likelihood Estimator ◽  
Birth Process ◽  
Design Problems ◽  
Continuous Sampling ◽  
Optimal Procedure

Let Y(t) be a pure birth process. If a maximum likelihood estimator of the birth intensity is desired and the number n of observational points and the last observation T are given in advance, it is shown that equidistant sampling is not an optimal procedure. Properties of ‘optimal' designs and the corresponding maximum likelihood estimators are investigated and compared with equidistant and continuous sampling.

Design problems for the pure birth process

1983 ◽  
Vol 15 (02) ◽  
pp. 255-273 ◽  
Author(s):  
Gerhard Becker ◽  
Götz Kersting
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Maximum Likelihood Estimators ◽  
Optimal Designs ◽  
Likelihood Estimator ◽  
Birth Process ◽  
Design Problems ◽  
Continuous Sampling ◽  
Optimal Procedure

Let Y(t) be a pure birth process. If a maximum likelihood estimator of the birth intensity is desired and the number n of observational points and the last observation T are given in advance, it is shown that equidistant sampling is not an optimal procedure. Properties of ‘optimal' designs and the corresponding maximum likelihood estimators are investigated and compared with equidistant and continuous sampling.

Sign in / Sign up

Export Citation Format

Share Document