Search Results - subject:"EM ALGORITHM"

A generative model for rank data based on insertion sort algorithm

Biernacki, Christophe; Jacques, Julien - In: Computational Statistics & Data Analysis 58 (2013) C, pp. 162-176

An original and meaningful probabilistic generative model for full rank data modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being...

Persistent link: https://www.econbiz.de/10010871334

Lognormal lifetimes and likelihood-based inference for flexible cure rate models based on COM-Poisson family

Balakrishnan, N.; Pal, Suvra - In: Computational Statistics & Data Analysis 67 (2013) C, pp. 41-67

the literature as special cases. Cancer clinical trials often lead to right censored data and so the EM algorithm can be … steps of the EM algorithm are developed here for the estimation of the parameters of different cure rate survival models …

Persistent link: https://www.econbiz.de/10010871353

Latent variable models for ordinal data by using the adaptive quadrature approximation

Cagnone, Silvia; Monari, Paola - In: Computational Statistics 28 (2013) 2, pp. 597-619

Latent variable models for ordinal data represent a useful tool in different fields of research in which the constructs of interest are not directly observable so that one or more latent variables are required to reduce the complexity of the data. In these cases problems related to the...

Persistent link: https://www.econbiz.de/10010847588

On estimation of measurement error models with replication under heavy-tailed distributions

Lin, Jin-Guan; Cao, Chun-Zheng - In: Computational Statistics 28 (2013) 2, pp. 809-829

Measurement error (errors-in-variables) models are frequently used in various scientific fields, such as engineering, medicine, chemistry, etc. In this work, we consider a new replicated structural measurement error model in which the replicated observations jointly follow scale mixtures of...

Persistent link: https://www.econbiz.de/10010847689

Inference on unknown parameters of a Burr distribution under hybrid censoring

Rastogi, Manoj; Tripathi, Yogesh - In: Statistical Papers 54 (2013) 3, pp. 619-643

XII distribution is taken up. The maximum likelihood estimates are developed for the unknown parameters using the EM … algorithm. Fisher information matrix is obtained by applying missing value principle and is further utilized for constructing …

Persistent link: https://www.econbiz.de/10010848046

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

Weissman, Alexander - In: Psychometrika 78 (2013) 1, pp. 134-153

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function … global convergence of the EM algorithm is attainable are provided in an information-theoretic context by interpreting the EM … algorithm as alternating minimization of the Kullback–Leibler divergence between two convex sets. It is shown that these …

Persistent link: https://www.econbiz.de/10010848139

Estimating depth of reasoning in a repeated guessing game with no feedback

Runco, Mariano - In: Experimental Economics 16 (2013) 3, pp. 402-413

This paper estimates depth of reasoning in an Iterative Best Response model using data from Weber (<CitationRef CitationID="CR20">2003</CitationRef>) ten-period repeated guessing game with no feedback. Different mixture models are estimated and the type (Level-0, Level-1, etc) of each player is determined in every round using the...</citationref>

Persistent link: https://www.econbiz.de/10010988970

A fast algorithm for robust constrained clustering

Fritz, Heinrich; García-Escudero, Luis A.; Mayo-Iscar, … - In: Computational Statistics & Data Analysis 61 (2013) C, pp. 124-136

The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and the fast-MCD algorithm. Despite this coincidence, it is not completely straightforward to combine both algorithms for developing a clustering method which is not severely affected by few...

Persistent link: https://www.econbiz.de/10011056525

Efficient maximum likelihood estimation of multiple membership linear mixed models, with an application to educational value-added assessments

Karl, Andrew T.; Yang, Yan; Lohr, Sharon L. - In: Computational Statistics & Data Analysis 59 (2013) C, pp. 13-27

computational challenges that have limited the model’s availability. We develop an EM algorithm to compute maximum likelihood …

Persistent link: https://www.econbiz.de/10011056582

Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting

Poncela, Marta; Poncela, Pilar; Perán, José Ramón - In: Applied Energy 108 (2013) C, pp. 349-362

Wind energy has the advantages of being clean, having a zero-cost primary energy source (wind) and having low operating and maintenance costs. Despite these advantages, it is difficult to manage due to its variable condition. Recently, there has been an explosion of forecasting tools to...

Persistent link: https://www.econbiz.de/10011041089