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Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

This book collects select chapters on modern industrial problems related to uncertainties and vagueness in the expert domain of knowledge. The book further provides the knowledge related to application of various mathematical and statistical tools in these areas. The results presented in the book help the researchers and scientists in handling complicated projects in their domains. Useful to industrialists, academicians, researchers and students alike, the book aims to help managers and technical specialists in designing and implementation of reliability and risk programs as below: Ensure the system safety and risk informed asset management Follow a proper strategy to maintain the mechanical components of the system Schedule the proper actions throughout the product life cycle Understand the structure and cost of a complex system Plan the proper schedule to improve the reliability and life of the system Identify unwanted failures and set up preventive and correction action

Front Cover -- Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Chapter 1: Introduction -- 1.1: Interplay of Psychology and Physics: Historical Overview -- 1.2: Quantum Brain -- 1.3: Quantum-Like Modeling of Cognition and Decision Making -- 1.3.1: From Probabilistic Foundations of Quantum Mechanics to Quantum-Like Modeling -- 1.3.2: Quantum-Like Models Outside Physics -- 1.4: Operational Formalism: Creation and Annihilation Operators -- 1.5: Social Laser as a Fruit of the Quantum Information Revolution

"This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extedible to cross-section data sets." "An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource."--Jacket

Front Matter -- Introduction / Daniel Pęa, George C Tiao -- Basic Concepts in Univariate Time Series. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, and State-Space Model / G Tunnicliffe Wilson -- Univariate Autoregressive Moving-Average Models / George C Tiao -- Model Fitting and Checking, and the Kalman Filter / G Tunnicliffe Wilson -- Prediction and Model Selection / Daniel Pęa -- Outliers, Influential Observations, and Missing Data / Daniel Pęa -- Automatic Modeling Methods for Univariate Series / Victor G̤mez, Agust̕n Maravall -- Seasonal Adjustment and Signal Extraction Time Series / Victor G̤mez, Agust̕n Maravall -- Advanced Topics in Univariate Time Series. Heteroscedastic Models / Ruey S Tsay -- Nonlinear Time Series Models: Testing and Applications / Ruey S Tsay -- Bayesian Time Series Analysis / Ruey S Tsay -- Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression, and Quantile Regression / Siegfried Heiler -- Neural Network Models / Kurt Hornik, Friedrich Leisch -- Multivariate Time Series. Vector ARMA Models / George C Tiao -- Cointegration in the VAR Model / S̜ren Johansen -- Identification of Linear Dynamic Multiinput/Multioutput Systems / Manfred Deistler -- Index -- Wiley Series in Probability and Statistics.

В статията са представени идеи за връзка между класическа и геометрична вероятност, които биха могли да подпомогнат преподавателите при операционализирането на учебната програма за общообразователна подготовка в ХІ клас. Най-общо казано, стохастично явление с безкрайно (неизброимо) много равновъзможни изходи се разлага на крайно обединение от равновероятни изходи. За това обединение е приложим моделът на класическата вероятност. Предимствата на такава методика са в по-добрата мотивация за въвеждане на новото понятие геометрична вероятност. Засегнати са и философски проблеми относно връзката между континуум и дискретна структура, които за целевата група носят мирогледно послание. Включени са примери от състезателните теми на турнира "Черноризец Храбър".

chapter 1 Geometric data analysis -- chapter 2 Correspondence analysis -- chapter 3 Multiple correspondence analysis -- chapter 4 Passive and supplementary points, supplementary variables and structured data analysis -- chapter 5 MCA and ascending hierarchical cluster analysis -- chapter 6 Constructing spaces -- chapter 7 Analyzing sub-groups: class-specific MCA.