scholarly journals Finite mixture of compositional regression with gaussian errors

2018 ◽  
Vol 41 (1) ◽  
pp. 75-86
Author(s):  
Taciana Shimizu ◽  
Francisco Louzada ◽  
Adriano Suzuki
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Regression Model ◽  
Simulation Study ◽  
Regression Models ◽  
Finite Mixture ◽  
Maximum Likelihood Estimates ◽  
The Real ◽  
Compositional Structure ◽  
Volleyball Players

In this paper, we consider to evaluate the efficiency of volleyball players according to the performance of attack, block and serve, but considering the compositional structure of the data related to the fundaments. The finite mixture of regression models better fitted the data in comparison with the usual regression model. The maximum likelihood estimates are obtained via an EM algorithm. A simulation study revels that the estimates are closer to the real values, the estimators are asymptotically unbiased for the parameters. A real Brazilian volleyball dataset related to the efficiency of the players is considered for the analysis.

scholarly journals Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model

2007 ◽  
Vol 7 (2) ◽  
pp. 167-182 ◽  
Author(s):  
Richard Chiburis ◽  
Michael Lokshin
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Regression Model ◽  
Selection Rule ◽  
Ordered Probit ◽  
Maximum Likelihood Estimates ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Two Step Estimation

We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.

Half Logistic Odd Weibull-Topp-Leone-G Family of Distributions: Model, Properties and Applications

2020 ◽  
Vol 15 (4) ◽  
pp. 2481-2510
Author(s):  
Fastel Chipepa ◽  
Divine Wanduku ◽  
Broderick Olusegun Oluyede
Keyword(s):  
Maximum Likelihood ◽  
Simulation Study ◽  
Real Data ◽  
Statistical Properties ◽  
Maximum Likelihood Estimates ◽  
Special Cases ◽  
Family Of Distributions

A new flexible and versatile generalized family of distributions, namely, half logistic odd Weibull-Topp-Leone-G (HLOW-TL-G) distribution is presented. The distribution can be traced back to the exponentiated-G distribution. We derive the statistical properties of the proposed family of distributions. Maximum likelihood estimates of the HLOW-TL-G family of distributions are also presented. Five special cases of the proposed family are presented. A simulation study and real data applications on one of the special cases are also presented

scholarly journals New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

2016 ◽  
Vol 5 (3) ◽  
pp. 9 ◽  
Author(s):  
Elizabeth M. Hashimoto ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega ◽  
G.G. Hamedani
Keyword(s):  
Regression Model ◽  
Censored Data ◽  
Survival Data ◽  
Regression Models ◽  
Empirical Distribution ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Standard Normal Distribution ◽  
Model Parameters ◽  
Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models. 

Finite Mixture Dynamic Regression Modeling of Panel Data With Implications for Dynamic Response Analysis

2005 ◽  
Vol 30 (2) ◽  
pp. 169-187 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Probability Density Function ◽  
Dynamic Response ◽  
Regression Model ◽  
Density Function ◽  
Regression Models ◽  
Linear Models ◽  
Finite Mixture ◽  
Response Analysis ◽  
Model Parameters ◽  
Mixture Density

This article considers the problem of estimating dynamic linear regression models when the data are generated from finite mixture probability density function where the mixture components are characterized by different dynamic regression model parameters. Specifically, conventional linear models assume that the data are generated by a single probability density function characterized by a single set of regression model parameters. However, when the true generating model is finite mixture density function, then estimation of conventional linear models under the assumption of a single density function may lead to erroneous conclusions. Instead, it may be desirable to estimate the regression model under the assumption that the data are derived from a finite mixture density function and to examine differences in the parameters of the model within each mixture component. Dynamic regression models and subsequent dynamic response analysis using dynamic multipliers are also likely to be affected by the existence of a finite mixture density because dynamic multipliers are functions of the regression model parameters. Utilizing finite mixture modeling applied to two real data examples, this article shows that dynamic responses to changes in exogenous variables can be quite different depending on the number and nature of underlying mixture components. Implications for substantive conclusions based on the use of dynamic multipliers is discussed.

scholarly journals Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples

2022 ◽  
Author(s):  
Lenore Pipes ◽  
Zihao Chen ◽  
Svetlana Afanaseva ◽  
Rasmus Nielsen
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Initial Step ◽  
Sequencing Depth ◽  
Maximum Likelihood Estimates ◽  
Reference Database ◽  
Wastewater Samples ◽  
Different Strains

Wastewater surveillance has become essential for monitoring the spread of SARS-CoV-2. The quantification of SARS-CoV-2 RNA in wastewater correlates with the Covid-19 caseload in a community. However, estimating the proportions of different SARS-CoV-2 strains has remained technically difficult. We present a method for estimating the relative proportions of SARS-CoV-2 strains from wastewater samples. The method uses an initial step to remove unlikely strains, imputation of missing nucleotides using the global SARS-CoV-2 phylogeny, and an Expectation-Maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different strains in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions accurately reflect the true proportions given sufficiently high sequencing depth and that the phylogenetic imputation is highly accurate and substantially improves the reference database.

scholarly journals A New Extended Birnbaum–Saunders Model: Properties, Regression and Applications

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 32-47
Author(s):  
Gauss Cordeiro ◽  
Maria de Lima ◽  
Edwin Ortega ◽  
Adriano Suzuki
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Real Data ◽  
Random Variable ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Poisson Random Variable ◽  
Fatigue Lifetime ◽  
Special Cases ◽  
New Distribution

We propose an extended fatigue lifetime model called the odd log-logistic Birnbaum–Saunders–Poisson distribution, which includes as special cases the Birnbaum–Saunders and odd log-logistic Birnbaum–Saunders distributions. We obtain some structural properties of the new distribution. We define a new extended regression model based on the logarithm of the odd log-logistic Birnbaum–Saunders–Poisson random variable. For censored data, we estimate the parameters of the regression model using maximum likelihood. We investigate the accuracy of the maximum likelihood estimates using Monte Carlo simulations. The importance of the proposed models, when compared to existing models, is illustrated by means of two real data sets.

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