Bayesian modeling of location, sale, and shape parameters in skew‐normal regression models

2021 ◽  
Author(s):  
Martha Luía Corrales ◽  
Edilberto Cepeda‐Cuervo
Keyword(s):  
Bayesian Modeling ◽  
Regression Models ◽  
Shape Parameters ◽  
Normal Regression ◽  
Skew Normal

Likelihood-Based Inference for Multivariate Skew-Normal Regression Models

2007 ◽  
Vol 36 (9) ◽  
pp. 1769-1786 ◽  
Author(s):  
Víctor H. Lachos ◽  
Heleno Bolfarine ◽  
Reinaldo B. Arellano-Valle ◽  
Lourdes C. Montenegro
Keyword(s):  
Regression Models ◽  
Normal Regression ◽  
Skew Normal

Homogeneity diagnostics for skew-normal nonlinear regression models

2009 ◽  
Vol 79 (6) ◽  
pp. 821-827 ◽  
Author(s):  
Feng-Chang Xie ◽  
Bo-Cheng Wei ◽  
Jin-Guan Lin
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Nonlinear Regression Models ◽  
Skew Normal

scholarly journals Star Positions from Overlapping Plates in the Cape Zone −40° to −52°

1974 ◽  
Vol 61 ◽  
pp. 305-306
Author(s):  
S. V. M. Clube
Keyword(s):  
Regression Models ◽  
Preliminary Results ◽  
Normal Regression

A modification of the normal regression models used for the determination of star positions is described. Some preliminary results relating to accuracy are given.

scholarly journals Statistical tests for latent class in censored data due to detection limit

2019 ◽  
Vol 29 (8) ◽  
pp. 2179-2197
Author(s):  
Hua He ◽  
Wan Tang ◽  
Tanika Kelly ◽  
Shengxu Li ◽  
Jiang He
Keyword(s):  
Regression Model ◽  
Normal Distribution ◽  
Regression Models ◽  
Latent Class ◽  
Limit Of Detection ◽  
Statistical Tests ◽  
Ratio Test ◽  
Tobit Regression ◽  
Normal Regression ◽  
Concentration Levels

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.

scholarly journals Variations of power-expected-posterior priors in normal regression models

2020 ◽  
Vol 143 ◽  
pp. 106836
Author(s):  
Dimitris Fouskakis ◽  
Ioannis Ntzoufras ◽  
Konstantinos Perrakis
Keyword(s):  
Regression Models ◽  
Normal Regression

Bayesian spatial regression models with closed skew normal correlated errors and missing observations

2010 ◽  
Vol 53 (1) ◽  
pp. 205-218 ◽  
Author(s):  
Omid Karimi ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Missing Observations ◽  
Correlated Errors ◽  
Spatial Regression Models ◽  
Skew Normal

Bayesian modeling and prior sensitivity analysis for zero–one augmented beta regression models with an application to psychometric data

2020 ◽  
Vol 34 (2) ◽  
pp. 304-322
Author(s):  
Danilo Covaes Nogarotto ◽  
Caio Lucidius Naberezny Azevedo ◽  
Jorge Luis Bazán
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Modeling ◽  
Regression Models ◽  
Beta Regression ◽  
Psychometric Data

scholarly journals Bimodal Regression Model

2017 ◽  
Vol 40 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Guillermo Domingo Martinez ◽  
Heleno Bolfarine ◽  
Hugo Salinas
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Error Term ◽  
Bimodal Distribution ◽  
New Model ◽  
Special Cases ◽  
Error Distributions ◽  
New Distribution ◽  
Skew Normal

Regression analysis is a technique widely used in different areas ofhuman knowledge, with distinct distributions for the error term. Itis the case, however, that regression models with bimodal responsesor, equivalently, with the error term following a bimodal distribution are notcommon in the literature, perhaps due to the lack of simple to dealwith bimodal error distributions. In this paper we propose a simpleto deal with bimodal regression model with a symmetric-asymmetricdistribution for the error term for which for some values of theshape parameter it can be bimodal. This new distribution containsthe normal and skew-normal as special cases. A realdata application reveals that the new model can be extremely usefulin such situations.

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