scholarly journals Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 590
Author(s):  
Ang Shan ◽  
Fengkai Yang
Keyword(s):  
Em Algorithm ◽  
Gibbs Sampling ◽  
Real Data ◽  
Homogeneous Groups ◽  
Mixture Regression ◽  
Explanatory Variables ◽  
Normal Regression ◽  
The Em Algorithm ◽  
Weak Points ◽  
Mixture Regression Model

Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gibbs sampling to deal with this model have several weak points. In this paper, a non-iterative sampling algorithm for fitting FMNR model is proposed from a Bayesian perspective. The procedure can generate independently and identically distributed samples from the posterior distributions of the parameters and produce more reliable estimations than the EM algorithm and Gibbs sampling. Simulation studies are conducted to illustrate the performance of the algorithm with supporting results. Finally, a real data is analyzed to show the usefulness of the methodology.

scholarly journals Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

2003 ◽  
Vol 37 (3-4) ◽  
pp. 365-375 ◽  
Author(s):  
S.K. Ng ◽  
K.K.W. Yau ◽  
A.H. Lee
Keyword(s):  
Length Of Stay ◽  
Em Algorithm ◽  
Mixture Regression ◽  
The Em Algorithm ◽  
Inpatient Length Of Stay

scholarly journals A Learning-Based EM Clustering for Circular Data with Unknown Number of Clusters

2020 ◽  
Vol 15 ◽  
pp. 42-51
Author(s):  
Shou-Jen Chang-Chien ◽  
Wajid Ali ◽  
Miin-Shen Yang
Keyword(s):  
Em Algorithm ◽  
A Priori ◽  
Real Data ◽  
Circular Data ◽  
Data Sets ◽  
Number Of Clusters ◽  
The Em Algorithm ◽  
Von Mises ◽  
Migrating Birds ◽  
Em Clustering

Clustering is a method for analyzing grouped data. Circular data were well used in various applications, such as wind directions, departure directions of migrating birds or animals, etc. The expectation & maximization (EM) algorithm on mixtures of von Mises distributions is popularly used for clustering circular data. In general, the EM algorithm is sensitive to initials and not robust to outliers in which it is also necessary to give a number of clusters a priori. In this paper, we consider a learning-based schema for EM, and then propose a learning-based EM algorithm on mixtures of von Mises distributions for clustering grouped circular data. The proposed clustering method is without any initial and robust to outliers with automatically finding the number of clusters. Some numerical and real data sets are used to compare the proposed algorithm with existing methods. Experimental results and comparisons actually demonstrate these good aspects of effectiveness and superiority of the proposed learning-based EM algorithm.

scholarly journals Influence Diagnostics in Log-Normal Regression Model with Censored Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Javeria Khaleeq ◽  
Muhammad Amanullah ◽  
Zahra Almaspoor
Keyword(s):  
Regression Model ◽  
Real Data ◽  
Biological Data ◽  
Skewed Distribution ◽  
Explanatory Variables ◽  
Normal Regression ◽  
Influence Diagnostics ◽  
One Step ◽  
The One ◽  
Log Normal

Dealing with the biological data, the skewed distribution is approximated by the Log-Normal Regression model (LNRM). Traditional estimation techniques for the LNRM are sensitive to unusual observations. These observations greatly affect the model analysis, which makes imprecise conclusions. To overcome this issue, we proposed to develop diagnostics measures based on local influence diagnostics to identify such curious observations in the LNRM under censoring. The proposed measures are derived by perturbing the case weight, response, and explanatory variables. Furthermore, we also consider the One-Step Newton-Raphson method and generalized cook’s distance. We study the Monte Carlo simulation and its application to real data to illustrate the developed approaches.

EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION

2016 ◽  
Vol 46 (3) ◽  
pp. 779-799 ◽  
Author(s):  
Cuihong Yin ◽  
X. Sheldon Lin
Keyword(s):  
Em Algorithm ◽  
Mixture Model ◽  
Penalty Function ◽  
Real Data ◽  
Efficient Estimation ◽  
Model Parameters ◽  
Simulation Studies ◽  
Distributional Properties ◽  
Data Application ◽  
The Em Algorithm

AbstractThe Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.

scholarly journals FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM

2015 ◽  
Vol 45 (3) ◽  
pp. 729-758 ◽  
Author(s):  
Roel Verbelen ◽  
Lan Gong ◽  
Katrien Antonio ◽  
Andrei Badescu ◽  
Sheldon Lin
Keyword(s):  
Em Algorithm ◽  
Simulated Data ◽  
Real Data ◽  
Data Sets ◽  
Truncated Data ◽  
The Em Algorithm ◽  
Loss Modeling ◽  
Censored And Truncated Data

AbstractWe discuss how to fit mixtures of Erlangs to censored and truncated data by iteratively using the EM algorithm. Mixtures of Erlangs form a very versatile, yet analytically tractable, class of distributions making them suitable for loss modeling purposes. The effectiveness of the proposed algorithm is demonstrated on simulated data as well as real data sets.

scholarly journals Robust mixture regression based on the skew t distribution

2017 ◽  
Vol 40 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Fatma Zehra Doğru ◽  
Olcay Arslan
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Simulation Study ◽  
Expectation Maximization ◽  
Real Data ◽  
Scale Mixture ◽  
Mixture Regression ◽  
Regression Procedure ◽  
Heavy Tailed ◽  
T Distribution

In this study, we propose a robust mixture regression procedure based on the skew t distribution to model heavy-tailed and/or skewed errors in a mixture regression setting. Using the scale mixture representation of the skew  t distribution, we give an Expectation Maximization (EM) algorithm to compute the maximum likelihood (ML) estimates for the paramaters of interest. The performance of proposed estimators is demonstrated by a simulation study and a real data example.

EVM: Speeding Up the EM Algorithm

2020 ◽  
Author(s):  
Mark Britten-Jones
Keyword(s):  
Em Algorithm ◽  
The Em Algorithm

Estimation and identification of time-varying long-term fading channels via the particle filter and the EM algorithm

2011 ◽  
Author(s):  
Xiao Ma ◽  
Mohammed M. Olama ◽  
Seddik M. Djouadi ◽  
Charalambos D. Charalambous
Keyword(s):  
Em Algorithm ◽  
Particle Filter ◽  
Fading Channels ◽  
Time Varying ◽  
The Em Algorithm

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

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