Bias Reduction of Approximate Maximum Likelihood Estimates for Heritability in Threshold Models

Biometrics ◽  
1998 ◽  
Vol 54 (3) ◽  
pp. 1155 ◽  
Author(s):  
Bas Engel ◽  
Willem Buist
Keyword(s):  
Maximum Likelihood ◽  
Bias Reduction ◽  
Maximum Likelihood Estimates ◽  
Threshold Models ◽  
Approximate Maximum Likelihood

Retention or Attrition Models

1989 ◽  
Vol 14 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Irwin Guttman ◽  
Ingram Olkin
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Analysis ◽  
Maximum Likelihood Estimates ◽  
Attrition Rates ◽  
Approximate Maximum Likelihood ◽  
Alternative Models

A model for retention and its counterpart, attrition, is presented. In a prototype example, students enter a program in each of k terms; some of the students complete the program, and the remainder leave. A key feature in the models proposed is that there is a dampening effect from term to term because the probability of leaving the program diminishes as the terms progress. The focus of this paper is the study of alternative models for the dampening in attrition rates. A number of alternative dampening effects are proposed that provide for different rates of attrition. Approximate maximum likelihood estimates for the underlying parameters in each model and a Bayesian analysis are provided.

scholarly journals Bias-Corrected Maximum Likelihood Estimators of the Parameters of the Unit-Weibull Distribution

2021 ◽  
Vol 50 (3) ◽  
pp. 41-53
Author(s):  
Andre Menezes ◽  
Josmar Mazucheli ◽  
F. Alqallaf ◽  
M. E. Ghitany
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Bias Reduction ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Analytical Methodology ◽  
Finite Samples ◽  
Modeling Data

It is well known that the maximum likelihood estimates (MLEs) have appealing statistical properties. Under fairly mild conditions their asymptotic distribution is normal, and no other estimator has a smaller asymptotic variance.However, in finite samples the maximum likelihood estimates are often biased estimates and the bias disappears as the sample size grows.Mazucheli, Menezes, and Ghitany (2018b) introduced a two-parameter unit-Weibull distribution which is useful for modeling data on the unit interval, however its MLEs are biased in finite samples.In this paper, we adopt three approaches for bias reduction of the MLEs of the parameters of unit-Weibull distribution.The first approach is the analytical methodology suggested by Cox and Snell (1968), the second is based on parametric bootstrap resampling method, and the third is the preventive approach introduced by Firth (1993).The results from Monte Carlo simulations revealed that the biases of the estimates should not be ignored and the bias reduction approaches are equally efficient. However, the first approach is easier to implement.Finally, applications to two real data sets are presented for illustrative purposes.

scholarly journals Median bias reduction of maximum likelihood estimates

Biometrika ◽  
2017 ◽  
Vol 104 (4) ◽  
pp. 923-938 ◽  
Author(s):  
E C Kenne Pagui ◽  
A Salvan ◽  
N Sartori
Keyword(s):  
Maximum Likelihood ◽  
Bias Reduction ◽  
Maximum Likelihood Estimates
Sign in / Sign up

Export Citation Format

Share Document