Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematical statistics --- 519.2 --- Probability. Mathematical statistics --- Latent structure analysis. --- Latent variables. --- 519.2 Probability. Mathematical statistics --- Latent structure analysis --- Latent variables --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Multivariate analysis --- Variables (Mathematics) --- Correlation (Statistics) --- Analyse des données

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis contributed essays to this volume, each presenting a key innovation to the basic latent class model and illustrating how it can prove useful in situations typically encountered in actual research.

Latent structure analysis. --- Latent variables. --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Correlation (Statistics) --- Latent variables --- #SBIB:022.AANKOOP --- #SBIB:303H520 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Academic collection --- Quantitative methods in social research --- Mathematical statistics --- Social Sciences --- Sociology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings. This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers. The chapters are based on the contributors’ research, with mathematical details minimized using applications-motivated descriptions. The first part of the book focuses on frequentist likelihood ratio and score tests for zero variance components. Contributors include Xihong Lin, Daowen Zhang and Ciprian Crainiceanu. The second part focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models. Contributors include David Dunson and collaborators Bo Cai and Saki Kinney. The final part focuses on structural equation models, with Peter Bentler and Jiajuan Liang presenting a frequentist approach, Sik-Yum Lee and Xin-Yuan Song presenting a Bayesian approach based on path sampling, and Joyee Ghosh and David Dunson proposing a method for default prior specification and efficient posterior computation. David Dunson is Professor in the Department of Statistical Science at Duke University. He is an international authority on Bayesian methods for correlated data, a fellow of the American Statistical Association, and winner of the David Byar and Mortimer Spiegelman Awards.

Statistics. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Electronic books. -- local. --- Latent variables. --- Random data (Statistics). --- Latent variables --- Random data (Statistics) --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Data, Random (Statistics) --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Statistics --- Stochastic processes --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Distribution (Probability theory. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The book, belonging to the series “Studies in Theoretical and Applied Statistics – Selected Papers from the Statistical Societies”, presents a peer-reviewed selection of contributions on relevant topics organized by the editors on the occasion of the SIS 2013 Statistical Conference "Advances in Latent Variables. Methods, Models and Applications", held at the Department of Economics and Management of the University of Brescia from June 19 to 21, 2013. The focus of the book is on advances in statistical methods for analyses with latent variables. In fact, in recent years, there has been increasing interest in this broad research area from both a theoretical and an applied point of view, as the statistical latent variable approach allows the effective modeling of complex real-life phenomena in a wide range of research fields. A major goal of the volume is to bring together articles written by statisticians from different research fields, which present different approaches and experiences related to the analysis of unobservable variables and the study of the relationships between them.

Statistics. --- Statistics, general. --- Statistical Theory and Methods. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Mathematical statistics. --- Economics --- Statistique --- Statistique mathématique --- Latent variables. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics .

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Nerve sheath tumors can be a significant cause of morbidity for many patients. These include benign tumors such as schwannomas, diffuse and plexiform neurofibromas, and atypical neurofibromas, as well as the aggressive soft tissue sarcoma known as the malignant peripheral nerve sheath tumor (MPNST). Nerve sheath tumors occur sporadically and in the context of the clinical neuro-genetic tumor predisposition syndromes neurofibromatosis type 1 (NF1) and type 2 (NF2). Historically, the mainstay of treatment for nerve sheath tumors has been surgery. However, for both benign and malignant nerve sheath tumors, there is a high recurrence rate, highlighting the pressing need for novel therapies. As we have entered the genomic era, the hope is that an improved understanding of the genetics, and therefore the biology, of these tumors will ultimately lead to therapies that result in better outcomes. In this Special Issue, we include both review articles and original research related to the genomic understanding and modeling of schwannomas, plexiform and diffuse neurofibromas, atypical neurofibromas, and malignant peripheral nerve sheath tumors as well as genomic methods being developed and applied to advance our understanding of these tumors.

Medicine --- neurofibromatosis type 1 --- nerve sheath tumor --- cancer --- latent variables --- machine learning --- supervised learning --- transfer learning --- random forest --- metaVIPER --- tumor deconvolution --- neurofibromatosis --- malignant peripheral nerve sheath tumor --- MPNST --- polycomb repressive complex --- PRC2 --- NF1 --- kinase --- kinome adaptation --- kinome reprogramming --- MET --- MEK --- doxorubicin --- capmatinib --- tram --- genomics --- tumor evolution --- pathology --- next generation sequencing --- clinical genetics --- malignant peripheral nerve sheath tumors --- plexiform neurofibromas --- Schwann cells --- neurofibromatosis type 1 syndrome --- neurofibromin 1 --- genetically engineered mouse models --- heterogeneity --- CRISPR/Cas9 --- mouse models --- sarcoma --- tumor microenvironment --- neurofibromatosis 1 (NF1) --- mebendazole (MBZ) --- COX-2 inhibitor --- malignancy --- chemoprevention --- nerve sheath tumors --- neurofibromatosis type 1 --- nerve sheath tumor --- cancer --- latent variables --- machine learning --- supervised learning --- transfer learning --- random forest --- metaVIPER --- tumor deconvolution --- neurofibromatosis --- malignant peripheral nerve sheath tumor --- MPNST --- polycomb repressive complex --- PRC2 --- NF1 --- kinase --- kinome adaptation --- kinome reprogramming --- MET --- MEK --- doxorubicin --- capmatinib --- tram --- genomics --- tumor evolution --- pathology --- next generation sequencing --- clinical genetics --- malignant peripheral nerve sheath tumors --- plexiform neurofibromas --- Schwann cells --- neurofibromatosis type 1 syndrome --- neurofibromin 1 --- genetically engineered mouse models --- heterogeneity --- CRISPR/Cas9 --- mouse models --- sarcoma --- tumor microenvironment --- neurofibromatosis 1 (NF1) --- mebendazole (MBZ) --- COX-2 inhibitor --- malignancy --- chemoprevention --- nerve sheath tumors