scholarly journals Masses of Negative Multinomial Distributions: Application to Polarimetric Image Processing

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Philippe Bernardoff ◽  
Florent Chatelain ◽  
Jean-Yves Tourneret
Keyword(s):  
Image Processing ◽  
Maximum Likelihood ◽  
Closed Form ◽  
Maximum Likelihood Estimator ◽  
Maximum Likelihood Estimators ◽  
Polarization Degree ◽  
Likelihood Estimator ◽  
Unknown Parameters ◽  
The Masses

This paper derives new closed-form expressions for the masses of negative multinomial distributions. These masses can be maximized to determine the maximum likelihood estimator of its unknown parameters. An application to polarimetric image processing is investigated. We study the maximum likelihood estimators of the polarization degree of polarimetric images using different combinations of images.

A SIMPLE ESTIMATOR FOR THE WEIBULL SHAPE PARAMETER

2012 ◽  
Vol 12 (02) ◽  
pp. 395-402 ◽  
Author(s):  
MAHDI TEIMOURI ◽  
SARALEES NADARAJAH
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Closed Form ◽  
Maximum Likelihood Estimator ◽  
Shape Parameter ◽  
Scale Parameter ◽  
Maximum Likelihood Estimators ◽  
Simulation Studies ◽  
Likelihood Estimator

The Weibull distribution is the most popular model for lifetimes. However, the maximum likelihood estimators for the Weibull distribution are not available in closed form. In this note, we derive a simple, consistent, closed form estimator for the Weibull shape parameter. This estimator is independent of the Weibull scale parameter. Simulation studies show that this estimator performs as well as the maximum likelihood estimator.

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

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.

scholarly journals Parametric Bootstrapping Predictive Estimator for Logistic Regression

2019 ◽  
pp. 1-15
Author(s):  
Kunio Takezawa
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Cross Validation ◽  
Bootstrap Method ◽  
Maximum Likelihood Estimators ◽  
Parametric Bootstrap ◽  
Likelihood Estimator ◽  
Parametric Bootstrapping

This paper proposes a method for constructing a predictive estimator for logistic regression. We make a provisional assumption that the predictive estimator is given by multiplying the maximum likelihood estimators by constants, which are estimated using a parametric bootstrap method. The relative merits of the maximum likelihood estimator and the predictive estimator produced by this method are determined by cross-validation. The results show that the predictiveestimators derived by this method lead to a smaller deviance than that obtained by the maximum likelihood estimator in many instances.

scholarly journals Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator

2020 ◽  
Author(s):  
Anand Deo ◽  
Sandeep Juneja
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Credit Risk ◽  
Closed Form ◽  
Maximum Likelihood Estimator ◽  
Likelihood Estimator ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator

Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults

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