scholarly journals Simultaneous Comparison of Sensitivities and Specificities of Two Diagnostic Tests Adjusting for Discrete Covariates

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2029
Author(s):  
José Antonio Roldán-Nofuentes
Keyword(s):  
Maximum Likelihood ◽  
Diagnostic Tests ◽  
Maximum Likelihood Method ◽  
Hypothesis Test ◽  
Likelihood Method ◽  
Simulation Experiments ◽  
Asymptotic Behaviors ◽  
General Rules ◽  
Simultaneous Comparison

Adjusting for covariates is important in the study of the performance of diagnostic tests. In this manuscript, the simultaneous comparison of the sensitivities and specificities of two binary diagnostic tests is studied when discrete covariates are observed in all of the individuals in the sample. Four methods are presented to simultaneously compare the two sensitivities and the two specificities: a global hypothesis test and three other methods based on individual comparisons. The maximum likelihood method was applied to adjust the overall estimators of sensitivities and specificities. Simulation experiments were carried out to study the asymptotic behaviors of the four proposed methods when the covariate is binary, giving general rules of application. The results were applied to a real example.

scholarly journals Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1694
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Regad
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Diagnostic Test ◽  
Gold Standard ◽  
Maximum Likelihood Method ◽  
Disease Prevalence ◽  
Kappa Coefficient ◽  
Likelihood Method ◽  
Simulation Experiments ◽  
Asymptotic Behaviors

The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.

scholarly journals A Unimodal/Bimodal Skew/Symmetric Distribution Generated from Lambert’s Transformation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 269
Author(s):  
Yuri A. Iriarte ◽  
Mário de Castro ◽  
Héctor W. Gómez
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Structural Properties ◽  
Maximum Likelihood Method ◽  
Bimodal Distribution ◽  
Symmetric Distribution ◽  
Likelihood Method ◽  
Simulation Experiments ◽  
Special Cases ◽  
New Distribution

The generalized bimodal distribution is especially efficient in modeling univariate data exhibiting symmetry and bimodality. However, its performance is poor when the data show important levels of skewness. This article introduces a new unimodal/bimodal distribution capable of modeling different skewness levels. The proposal arises from the recently introduced Lambert transformation when considering a generalized bimodal baseline distribution. The bimodal-normal and generalized bimodal distributions can be derived as special cases of the new distribution. The main structural properties are derived and the parameter estimation is carried out under the maximum likelihood method. The behavior of the estimators is assessed through simulation experiments. Finally, two applications are presented in order to illustrate the utility of the proposed distribution in data modeling in different real settings.

scholarly journals A New Probability Distribution for Modeling Failure and Service Times: Properties, Copulas and Various Estimation Methods

2021 ◽  
Vol 9 (3) ◽  
pp. 555-586
Author(s):  
Hanaa Elgohari ◽  
Mohamed Ibrahim ◽  
Haitham Yousof
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Real Data ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Finite Sample ◽  
Simulation Experiments ◽  
Mean Squared Errors

In this paper, a new generalization of the Pareto type II model is introduced and studied. The new density canbe “right skewed” with heavy tail shape and its corresponding failure rate can be “J-shape”, “decreasing” and “upside down (or increasing-constant-decreasing)”. The new model may be used as an “under-dispersed” and “over-dispersed” model. Bayesian and non-Bayesian estimation methods are considered. We assessed the performance of all methods via simulation study. Bayesian and non-Bayesian estimation methods are compared in modeling real data via two applications. In modeling real data, the maximum likelihood method is the best estimation method. So, we used it in comparing competitive models. Before using the the maximum likelihood method, we performed simulation experiments to assess the finite sample behavior of it using the biases and mean squared errors.

Stochastic Maximum-Likelihood Method for MIMO Propagation Parameter Estimation

2007 ◽  
Vol 55 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Cssio B. Ribeiro ◽  
Esa Ollila ◽  
Visa Koivunen
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Propagation Parameter

The Filtration of Composite Signals from Interference by the Maximum Likelihood Method

2017 ◽  
pp. 629-634
Author(s):  
Kseniia V. Vlasova ◽  
Valerii A. Pakhotin ◽  
Evgenii V. Korotey ◽  
Sergey B. Makarov
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method
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