Finite-Sample Bias of the Conditional Gaussian Maximum Likelihood Estimator in ARMA Models

2016 ◽  
pp. 207-244 ◽  
Author(s):  
Yong Bao
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Finite Sample ◽  
Arma Models ◽  
Likelihood Estimator ◽  
Sample Bias ◽  
Finite Sample Bias

Using ranked set sampling with extreme ranks in estimating the population proportion

2019 ◽  
Vol 29 (1) ◽  
pp. 165-177 ◽  
Author(s):  
Ehsan Zamanzade ◽  
M Mahdizadeh
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Finite Sample ◽  
Likelihood Estimator ◽  
Population Proportion ◽  
Health And Nutrition ◽  
Finite Sample Size ◽  
Using Data

This article studies the properties of the maximum likelihood estimator of the population proportion in ranked set sampling with extreme ranks. The maximum likelihood estimator is described and its asymptotic distribution is derived. Finite sample size properties of the estimator are investigated using simulation studies. It turns out that the proposed estimator is substantially more efficient than its simple random sampling and ranked set sampling analogs, as the true population proportion tends to zero/unity. The method is illustrated using data from the National Health and Nutrition Examination Survey.

Approximating the Finite Sample Bias for Maximum Likelihood Estimators by Using the Score

1996 ◽  
Vol 12 (1) ◽  
pp. 199-199
Author(s):  
Bart Lambrech ◽  
William Perraudin ◽  
Stephen Satchell
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Finite Sample ◽  
Sample Bias ◽  
Finite Sample Bias
Sign in / Sign up

Export Citation Format

Share Document