Strong Consistency of Maximum-Likelihood Estimates

1996 ◽  
pp. 112-118
Author(s):  
Thomas S. Ferguson
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimates

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

scholarly journals Directional Selection and the Site-Frequency Spectrum

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1779-1788 ◽  
Author(s):  
Carlos D Bustamante ◽  
John Wakeley ◽  
Stanley Sawyer ◽  
Daniel L Hartl
Keyword(s):  
Maximum Likelihood ◽  
High Power ◽  
Negative Selection ◽  
Likelihood Estimation ◽  
Directional Selection ◽  
Likelihood Ratio Tests ◽  
Maximum Likelihood Estimates ◽  
Likelihood Methods ◽  
Ancestral States ◽  
Asymptotic Variances

Abstract In this article we explore statistical properties of the maximum-likelihood estimates (MLEs) of the selection and mutation parameters in a Poisson random field population genetics model of directional selection at DNA sites. We derive the asymptotic variances and covariance of the MLEs and explore the power of the likelihood ratio tests (LRT) of neutrality for varying levels of mutation and selection as well as the robustness of the LRT to deviations from the assumption of free recombination among sites. We also discuss the coverage of confidence intervals on the basis of two standard-likelihood methods. We find that the LRT has high power to detect deviations from neutrality and that the maximum-likelihood estimation performs very well when the ancestral states of all mutations in the sample are known. When the ancestral states are not known, the test has high power to detect deviations from neutrality for negative selection but not for positive selection. We also find that the LRT is not robust to deviations from the assumption of independence among sites.

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