Search Results

You are looking at 1 - 10 of 992 items :

  • "mixture models" x
Clear All

-953. Harrell F.E. (2001): Regression Modelling Strategies. Springer-Verlag, New York. Komarek A., Lesa re E., Hilton J.F. (2005): Accelerated failure time model for arbitrarily censored data with smoothed error distribution. Journal of Computational and Graphical Statistics 14: 726-745. Lee Y., Nelder J.A., Pawitan Y. (2006): Generalized Linear Models with Random E ects. Chapman & Hall / CRC: Boca Raton. McLachlan G.J., Peel D. (2000): Finite Mixture Models. John Wiley and Sons, New York. Muthen B., Brown H.C. (2009): Estimating drug e ects in the presence of placebo

continuous variable, calculated as the average of several replicates (i.e., several beads per sample), and lying between zero (unmethylated) and one (methylated). Unsupervised clustering of DNA methylation data is often used for the identification of methylation subgroups, or groups of samples with a similar methylation profile across a collection CpGs. Although there is no universal consensus on the best clustering method for array-based DNA methylation data, Siegmund et al. (2003) argue that model-based methods for clustering via finite mixture models are preferred to

Volume 4, Issue 1 2008 Article 20 The International Journal of Biostatistics A Marginal Mixture Model for Selecting Differentially Expressed Genes across Two Types of Tissue Samples Weiliang Qiu, Brigham and Women's Hospital and Harvard Medical School Wenqing He, University of Western Ontario Xiaogang Wang, York University Ross Lazarus, Brigham and Women's Hospital and Harvard Medical School Recommended Citation: Qiu, Weiliang; He, Wenqing; Wang, Xiaogang; and Lazarus, Ross (2008) "A Marginal Mixture Model for Selecting Differentially Expressed Genes across Two

Volume 4, Issue 2 2010 Article 5 Asia-Pacific Journal of Risk and Insurance Survival Mixture Model for Credit Risk Analysis Leo S. F. Mo, City University of Hong Kong Kelvin K. W. Yau, City University of Hong Kong Recommended Citation: Mo, Leo S. F. and Yau, Kelvin K. W. (2010) "Survival Mixture Model for Credit Risk Analysis," Asia-Pacific Journal of Risk and Insurance: Vol. 4: Iss. 2, Article 5. DOI: 10.2202/2153-3792.1061 ©2010 Asia-Pacific Risk and Insurance Association. All rights reserved. Survival Mixture Model for Credit Risk Analysis Leo S. F. Mo and

1 Introduction In immune response studies, statistical mixture modelling is becoming established for analysis of increasingly large data sets from flow cytometry technologies (e.g., Chan et al., 2008; Lo et al., 2008; Finak et al., 2009; Pyne et al., 2009; Manolopoulou et al., 2010). Core interests lie in identifying and resolving multiple subtypes of immune cells, differentiated by the levels of activity (and presence/absence) of subsets of cell surface receptor molecules, as well as other phenotypic markers of cell phenotypes. Flow cytometry (FCM) technology

Volume 9, Issue 1 2010 Article 19 Statistical Applications in Genetics and Molecular Biology Sub-Modular Resolution Analysis by Network Mixture Models Elisabetta Marras, CRS4 Bioinformatics Lab Antonella Travaglione, CRS4 Bioinformatics Lab Enrico Capobianco, CRS4 Bioinformatics Lab Recommended Citation: Marras, Elisabetta; Travaglione, Antonella; and Capobianco, Enrico (2010) "Sub-Modular Resolution Analysis by Network Mixture Models," Statistical Applications in Genetics and Molecular Biology: Vol. 9: Iss. 1, Article 19. DOI: 10.2202/1544-6115.1523 Sub

References [1] CHIB, S.-GREENBERG, E.: Understanding the metropolis-hastings algorithm , Amer. Statist. 49 (1995), 327-335. [2] GELFAND, A. E.-SMITH, A. F. M.: Sampling-based approaches to calculating marginal densities , J. Amer. Statist. Assoc. 85 (1990), 398-409. [3] KLEIN, J. P.-MOESCHBERGER, M. L.: Survival Analysis: Techniques for Censored and Truncated Data. Springer-Verlag, New York, NY, 1997. [4] LAMBERT, P. C.-DICKMAN, P. W.-WESTON, C. L.-THOMPSON, J. R.: Estimating the cure fraction in population-based cancer studies by using finite mixture models

1 Introduction Finite mixture models provide a flexible semi-parametric approach to describe probability distributions. Probability distributions are the basis of model-based statistical data analysis. In this context, they are used to describe the generative model underlying the data. For biological data, distributions arise due to uncertainty and heterogeneity. The latter case is particularly interesting. Even in clonal populations of cells, cell-to-cell variability in gene expression, protein expression, and other quantifiable traits is ubiquitous and results

listed in the Introduction Section. Hence, in this work, we are motivated to consider four types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection. Further, the effectiveness of the Gaussian mixture model (GMM)–MFCC framework is evaluated for categorizing these dysfluencies. Table 1 Summary of Previous Research Works on Stuttering Recognition. First author Year Database Features Classifier Accuracy Howell [ 13 ] 1995 12 Speakers Envelop parameter ANNs 80% Howell [ 14 ] 1997 12 Speakers Duration, energy peaks ANNs 78

. Optim. Lett. 8 (7), 1999–2020. [9] Pavlikov, K. and S. Uryasev (2018). CVaR distance between univariate probability distributions and approximation problems. Ann. Oper. Res. 262 (1), 67–88. [10] Permuter, H., J. Francos, and I. Jermyn (2006). A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognit. 39 (4), 695–706. [11] Rockafellar, R. T. and S. Uryasev (2000). Optimization of conditional value-at-risk. J. Risk 2 (3), 21–41. [12] Rockafellar, R. T. and S. Uryasev (2002). Conditional value