Limitations of Maximum Likelihood Estimation Procedures When a Majority of the Observations Are Below the Limit of Detection

2008 ◽  
Vol 80 (12) ◽  
pp. 4767-4772 ◽  
Author(s):  
Ram B. Jain ◽  
Richard Y. Wang
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Limit Of Detection ◽  
Likelihood Estimation ◽  
Estimation Procedures

scholarly journals Microbial-Maximum Likelihood Estimation Tool for Microbial Quantification in Food From Left-Censored Data Using Maximum Likelihood Estimation for Microbial Risk Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Gyung Jin Bahk ◽  
Hyo Jung Lee
Keyword(s):  
Risk Assessment ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Censored Data ◽  
Limit Of Detection ◽  
Likelihood Estimation ◽  
Food Microbiology ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk

In food microbial measurements, when most or very often bacterial counts are below to the limit of quantification (LOQ) or the limit of detection (LOD) in collected food samples, they are either ignored or a specified value is substituted. The consequence of this approach is that it may lead to the over or underestimation of quantitative results. A maximum likelihood estimation (MLE) or Bayesian models can be applied to deal with this kind of censored data. Recently, in food microbiology, an MLE that deals with censored results by fitting a parametric distribution has been introduced. However, the MLE approach has limited practical application in food microbiology as practical tools for implementing MLE statistical methods are limited. We therefore developed a user-friendly MLE tool (called “Microbial-MLE Tool”), which can be easily used without requiring complex mathematical knowledge of MLE but the tool is designated to adjust log-normal distributions to observed counts, and illustrated how this method may be implemented for food microbial censored data using an Excel spreadsheet. In addition, we used two case studies based on food microbial laboratory measurements to illustrate the use of the tool. We believe that the Microbial-MLE tool provides an accessible and comprehensible means for performing MLE in food microbiology and it will also be of help to improve the outcome of quantitative microbial risk assessment (MRA).

A Review of Estimation Procedures for the Rasch Model with an Eye toward Longish Tests

1980 ◽  
Vol 5 (1) ◽  
pp. 35-64 ◽  
Author(s):  
Howard Wainer ◽  
Anne Morgan ◽  
Jan-Eric Gustafsson
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Rasch Model ◽  
Likelihood Estimation ◽  
New Developments ◽  
Conditional Maximum Likelihood ◽  
English Writing ◽  
Estimation Procedures ◽  
Conditional Maximum ◽  
The Rasch Model

Two estimation procedures for the Rasch Model are reviewed in detail, particularly with respect to new developments that make the more statistically rigorous Conditional Maximum Likelihood estimation practical for use with longish tests. Emphasis of the review is on European developments which are not well known in the English writing world.

Maximum likelihood estimation of an across-regime correlation parameter

2021 ◽  
Vol 21 (2) ◽  
pp. 430-461
Author(s):  
Giorgio Calzolari ◽  
Maria Gabriella Campolo ◽  
Antonino Di Pino ◽  
Laura Magazzini
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Method ◽  
Likelihood Estimation ◽  
Regression Coefficients ◽  
Likelihood Method ◽  
Correlation Parameter ◽  
Switching Model ◽  
Endogenous Switching ◽  
Estimation Procedures

In this article, we describe the mlcar command, which implements a maximum likelihood method to simultaneously estimate the regression coefficients of a two-regime endogenous switching model and the coefficient measuring the correlation of outcomes between the two regimes. This coefficient, known as the “across-regime” correlation parameter, is generally unidentified in the traditional estimation procedures.

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