Random Scaling Factors In Bayesian Distributional Regression Models With An Application To Real Estate Data

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Random Scaling Factors in Bayesian Distributional Regression Models with an Application to Real Estate Data

Author : Alexander Razen
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (961 download)

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Book Synopsis Random Scaling Factors in Bayesian Distributional Regression Models with an Application to Real Estate Data by : Alexander Razen

Download or read book Random Scaling Factors in Bayesian Distributional Regression Models with an Application to Real Estate Data written by Alexander Razen and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributional structured additive regression provides a flexible framework for modeling each parameter of a potentially complex response distribution in dependence of covariates. Structured additive predictors allow for an additive decomposition of covariate effects with nonlinear effects and time trends, unit- or cluster- specific heterogeneity, spatial heterogeneity and complex interactions between co- variates of different type. Within this framework, we present a simultaneous estimation approach for multiplicative random effects that allow for cluster-specific heterogeneity with respect to the scaling of a covariate's effect. More specifically, a possibly nonlinear function f (z) of a covariate z may be scaled by a multiplicative cluster-specific random effect (1 + ac). Inference is fully Bayesian and is based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach within extensive simulation experiments for different response distributions. Furthermore, we apply the methodology to German real estate data where we identify significant district- specific scaling factors. According to the deviance information criterion, the models incorporating these factors perform significantly better than standard models with- out random scaling factors.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Flexible Regression and Smoothing

Author : Mikis D. Stasinopoulos
Publisher : CRC Press
ISBN 13 : 1351980378
Total Pages : 641 pages
Book Rating : 4.3/5 (519 download)

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Book Synopsis Flexible Regression and Smoothing by : Mikis D. Stasinopoulos

Download or read book Flexible Regression and Smoothing written by Mikis D. Stasinopoulos and published by CRC Press. This book was released on 2017-04-21 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables. Key Features: Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R. Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning. R code integrated into the text for ease of understanding and replication. Supplemented by a website with code, data and extra materials. This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.

Learning Statistics with R

Author : Daniel Navarro
Publisher : Lulu.com
ISBN 13 : 1326189727
Total Pages : 617 pages
Book Rating : 4.3/5 (261 download)

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Book Synopsis Learning Statistics with R by : Daniel Navarro

Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Regression for Categorical Data

Author : Gerhard Tutz
Publisher : Cambridge University Press
ISBN 13 : 1139499580
Total Pages : 573 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Regression for Categorical Data by : Gerhard Tutz

Download or read book Regression for Categorical Data written by Gerhard Tutz and published by Cambridge University Press. This book was released on 2011-11-21 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

Scientific and Technical Aerospace Reports

Author :
Publisher :
ISBN 13 :
Total Pages : 1280 pages
Book Rating : 4.:/5 (31 download)

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Book Synopsis Scientific and Technical Aerospace Reports by :

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1980 with total page 1280 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Approaches for Urban Environments

Author : Marco Helbich
Publisher : Springer
ISBN 13 : 3319114697
Total Pages : 394 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Computational Approaches for Urban Environments by : Marco Helbich

Download or read book Computational Approaches for Urban Environments written by Marco Helbich and published by Springer. This book was released on 2015-01-22 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to promote the synergistic usage of advanced computational methodologies in close relationship to geospatial information across cities of different scales. A rich collection of chapters subsumes current research frontiers originating from disciplines such as geography, urban planning, computer science, statistics, geographic information science and remote sensing. The topics covered in the book are of interest to researchers, postgraduates, practitioners and professionals. The editors hope that the scientific outcome of this book will stimulate future urban-related international and interdisciplinary research, bringing us closer to the vision of a “new science of cities.”

High-Dimensional Covariance Estimation

Author : Mohsen Pourahmadi
Publisher : John Wiley & Sons
ISBN 13 : 1118034295
Total Pages : 204 pages
Book Rating : 4.1/5 (18 download)

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Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi

Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Distributions for Modeling Location, Scale, and Shape

Author : Robert A. Rigby
Publisher : CRC Press
ISBN 13 : 100069996X
Total Pages : 589 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Distributions for Modeling Location, Scale, and Shape by : Robert A. Rigby

Download or read book Distributions for Modeling Location, Scale, and Shape written by Robert A. Rigby and published by CRC Press. This book was released on 2019-10-08 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. Key features: Describes over 100 distributions, (implemented in the GAMLSS packages in R), including continuous, discrete and mixed distributions. Comprehensive summary tables of the properties of the distributions. Discusses properties of distributions, including skewness, kurtosis, robustness and an important classification of tail heaviness. Includes mixed distributions which are continuous distributions with additional specific values with point probabilities. Includes many real data examples, with R code integrated in the text for ease of understanding and replication. Supplemented by the gamlss website. This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables, and to those interested in the properties of distributions.

Aspects of Uncertainty

Author : Adrian F. M. Smith
Publisher :
ISBN 13 :
Total Pages : 428 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Aspects of Uncertainty by : Adrian F. M. Smith

Download or read book Aspects of Uncertainty written by Adrian F. M. Smith and published by . This book was released on 1994-09-13 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Throughout his career Dennis Lindley has insisted on thinking things through from first principles and on basing developments on firm, logical foundations. Although his fundamental contributions to Bayesian statistics and decision theory are universally recognised, it is less well known that he arrived at the Bayesian position as a result of seeking to establish a rigorous axiomatic justification for classical statistical procedures.

Discrete Choice Methods with Simulation

Author : Kenneth Train
Publisher : Cambridge University Press
ISBN 13 : 0521766559
Total Pages : 399 pages
Book Rating : 4.5/5 (217 download)

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Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train

Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Current Index to Statistics, Applications, Methods and Theory

Author :
Publisher :
ISBN 13 :
Total Pages : 798 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Current Index to Statistics, Applications, Methods and Theory by :

Download or read book Current Index to Statistics, Applications, Methods and Theory written by and published by . This book was released on 1998 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.

Bayesian Computation with R

Author : Jim Albert
Publisher : Springer Science & Business Media
ISBN 13 : 0387922989
Total Pages : 304 pages
Book Rating : 4.3/5 (879 download)

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Book Synopsis Bayesian Computation with R by : Jim Albert

Download or read book Bayesian Computation with R written by Jim Albert and published by Springer Science & Business Media. This book was released on 2009-04-20 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Bayesian Structural Equation Modeling

Author : Sarah Depaoli
Publisher : Guilford Publications
ISBN 13 : 1462547745
Total Pages : 549 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Bayesian Structural Equation Modeling by : Sarah Depaoli

Download or read book Bayesian Structural Equation Modeling written by Sarah Depaoli and published by Guilford Publications. This book was released on 2021-08-16 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.

Doing Bayesian Data Analysis

Author : John Kruschke
Publisher : Academic Press
ISBN 13 : 0123814863
Total Pages : 673 pages
Book Rating : 4.1/5 (238 download)

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Book Synopsis Doing Bayesian Data Analysis by : John Kruschke

Download or read book Doing Bayesian Data Analysis written by John Kruschke and published by Academic Press. This book was released on 2010-11-25 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS software - Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). - Coverage of experiment planning - R and BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment

Encyclopedia of Ecology

Author : Brian D. Fath
Publisher : Elsevier
ISBN 13 : 0444641300
Total Pages : 2786 pages
Book Rating : 4.4/5 (446 download)

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Book Synopsis Encyclopedia of Ecology by : Brian D. Fath

Download or read book Encyclopedia of Ecology written by Brian D. Fath and published by Elsevier. This book was released on 2018-08-23 with total page 2786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Ecology, Second Edition, Four Volume Set continues the acclaimed work of the previous edition published in 2008. It covers all scales of biological organization, from organisms, to populations, to communities and ecosystems. Laboratory, field, simulation modelling, and theoretical approaches are presented to show how living systems sustain structure and function in space and time. New areas of focus include micro- and macro scales, molecular and genetic ecology, and global ecology (e.g., climate change, earth transformations, ecosystem services, and the food-water-energy nexus) are included. In addition, new, international experts in ecology contribute on a variety of topics. Offers the most broad-ranging and comprehensive resource available in the field of ecology Provides foundational content and suggests further reading Incorporates the expertise of over 500 outstanding investigators in the field of ecology, including top young scientists with both research and teaching experience Includes multimedia resources, such as an Interactive Map Viewer and links to a CSDMS (Community Surface Dynamics Modeling System), an open-source platform for modelers to share and link models dealing with earth system processes

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data