Search Results - subject:"EM algorithm"

A robust extension of the bivariate Birnbaum–Saunders distribution and associated inference

Vilca, Filidor; Balakrishnan, N.; Zeller, Camila Borelli - In: Journal of Multivariate Analysis 124 (2014) C, pp. 418-435

marginals are of univariate Birnbaum–Saunders type. We then develop the EM-algorithm for the maximum likelihood (ML) estimation …

Persistent link: https://www.econbiz.de/10011041898

Multivariate measurement error models using finite mixtures of skew-Student t distributions

Cabral, Celso Rômulo Barbosa; Lachos, Víctor Hugo; … - In: Journal of Multivariate Analysis 124 (2014) C, pp. 179-198

In regression models, the classical assumption of normal distribution of the random observational errors is often violated, masking some important features of the variability present in the data. Some practical actions to solve the problem, like transformation of variables to achieve normality,...

Persistent link: https://www.econbiz.de/10011041905

On bivariate Weibull-Geometric distribution

Kundu, Debasis; Gupta, Arjun K. - In: Journal of Multivariate Analysis 123 (2014) C, pp. 19-29

algorithm, and it is observed that the implementation of the EM algorithm is quite straightforward. Two data sets have been … analyzed for illustrative purposes, and it is observed that the new model and the proposed EM algorithm work quite well in … distribution has five parameters, and the maximum likelihood estimators cannot be obtained in closed form. We propose to use the EM …

Persistent link: https://www.econbiz.de/10011041954

Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Bartolucci, F.; Farcomeni, A.; Pennoni, F. - In: TEST: An Official Journal of the Spanish Society of … 23 (2014) 3, pp. 433-465

<Para ID="Par1">We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal categorical data. We illustrate the general version of the LM model which includes individual covariates, and several constrained versions. Constraints make the model more parsimonious and allow us...</para>

Persistent link: https://www.econbiz.de/10010994294

Bayesian approach for mixture models with grouped data

Gau, Shiow-Lan; Tapsoba, Jean Dieu; Lee, Shen-Ming - In: Computational Statistics 29 (2014) 5, pp. 1025-1043

Finite mixture modeling approach is widely used for the analysis of bimodal or multimodal data that are individually observed in many situations. However, in some applications, the analysis becomes substantially challenging as the available data are grouped into categories. In this work, we...

Persistent link: https://www.econbiz.de/10010998492

On heterogeneous latent class models with applications to the analysis of rating scores

Bertrand, Aurélie; Hafner, Christian - In: Computational Statistics 29 (2014) 1, pp. 307-330

Discovering the preferences and the behaviour of consumers is a key challenge in marketing. Information about such topics can be gathered through surveys in which the respondents must assign a score to a number of items. A strategy based on different latent class models can be used to analyze...

Persistent link: https://www.econbiz.de/10010998505

Maximum likelihood estimation under a finite mixture of generalized exponential distributions based on censored data

Ateya, Saieed - In: Statistical Papers 55 (2014) 2, pp. 311-325

and the maximum likelihood estimates (MLE’s) of the parameters are obtained using EM algorithm based on a general form of …

Persistent link: https://www.econbiz.de/10010998558

Robust errors-in-variables linear regression via Laplace distribution

Shi, Jianhong; Chen, Kun; Song, Weixing - In: Statistics & Probability Letters 84 (2014) C, pp. 113-120

Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed...

Persistent link: https://www.econbiz.de/10010718799

Marginal regression analysis of clustered failure time data with a cure fraction

Niu, Yi; Peng, Yingwei - In: Journal of Multivariate Analysis 123 (2014) C, pp. 129-142

matrices into an EM algorithm to estimate the regression coefficients and the baseline hazard function in the marginal model …

Persistent link: https://www.econbiz.de/10010718998

Parsimonious skew mixture models for model-based clustering and classification

Vrbik, Irene; McNicholas, Paul D. - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 196-210

Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skew-t and skew-normal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to...

Persistent link: https://www.econbiz.de/10010719663