Catalog Search Results
Author
Pub. Date
2013.
Language
English
Description
Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods.
Author
Series
Pub. Date
[2014]
Language
English
Description
"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the...
Author
Series
Pub. Date
2014.
Language
English
Description
"Accessible to students in statistics and industrial engineering, this text presents a systematic introduction to traditional and modern SPC methods. Requiring some background in basic linear algebra, calculus, and introductory statistics, the book illustrates the methods using detailed worked examples from the author's industrial experience. Pseudocode is provided for important methods and R code is available online. The text includes exercises at...
Author
Pub. Date
2014
Language
English
Description
Matrices, Vectors, and Their OperationsBasic definitions and notations Matrix addition and scalar-matrix multiplication Matrix multiplication Partitioned matricesThe ""trace"" of a square matrix Some special matricesSystems of Linear EquationsIntroduction Gaussian elimination Gauss-Jordan elimination Elementary matrices Homogeneous linear systems The inverse of a matrixMore on Linear EquationsThe LU decompositionCrout's Algorithm LU decomposition...
Author
Language
English
Description
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning...
Author
Series
Pub. Date
[2015]
Language
English
Description
"Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The...
Author
Pub. Date
2015
Language
English
Description
Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological Studies Spatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and environmental epidemiologists, the book links recent developments in spatio-temporal methodology with...
Author
Series
Pub. Date
[2015]
Language
English
Description
Statistics for Finance develops students professional skills in statistics with applications in finance. Developed from the authors courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book...
Author
Series
Pub. Date
[2016]
Language
English
Description
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their...
Author
Series
Pub. Date
2021.
Language
English
Description
Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references...
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