› › A Geoadditive Distributional Regression Analysis Of The Local Relationship Of Land Prices And Land Rents In Germany

The Economy as a Complex Spatial System

Author : Pasquale Commendatore

Publisher : Springer

Page : 227 pages

File Size : 29,29 MB

Release : 2017-09-18

Category : Science

ISBN : 3319656279

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Book Description

This book is open access under a CC BY-NC 4.0 license. This collected volume represents the final outcome of the COST Action IS1104 “The EU in the new complex geography of economic systems: models, tools and policy evaluation”. Visualizing the EU as a complex and multi-layered network, the book is organized in three parts, each of them dealing with a different level of analysis: At the macro-level, Part I considers the interactions within large economic systems (regions or countries) involving trade, workers migration, and other factor movements. At the meso-level, Part II discusses interactions within specific but wide-ranging markets, with a focus on financial markets and banking systems. Lastly, at the micro-level, Part III explores the decision-making of single firms, especially in the context of location decisions.



Effective Statistical Learning Methods for Actuaries I

Author : Michel Denuit

Publisher : Springer Nature

Page : 452 pages

File Size : 38,51 MB

Release : 2019-09-03

Category : Business & Economics

ISBN : 3030258203

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Book Description

This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.



Modeling Discrete Time-to-Event Data

Author : Gerhard Tutz

Publisher : Springer

Page : 252 pages

File Size : 10,52 MB

Release : 2016-06-14

Category : Mathematics

ISBN : 3319281585

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Book Description

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.



Flexible Regression and Smoothing

Author : Mikis D. Stasinopoulos

Publisher : CRC Press

Page : 641 pages

File Size : 42,14 MB

Release : 2017-04-21

Category : Mathematics

ISBN : 1351980378

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Book Description

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.



Distributions for Modeling Location, Scale, and Shape

Author : Robert A. Rigby

Publisher : CRC Press

Page : 544 pages

File Size : 21,53 MB

Release : 2019-10-08

Category : Mathematics

ISBN : 1000701182

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Book Description

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.



Nonparametric Regression and Generalized Linear Models

Author : P.J. Green

Publisher : CRC Press

Page : 197 pages

File Size : 43,47 MB

Release : 1993-05-01

Category : Mathematics

ISBN : 1482229757

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Book Description

Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique provides a unifying approach to a wide range of smoothing problems. The emphasis is methodological rather than theoretical, and the authors concentrate on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students.



What Drives Inequality?

Author : Koen Decancq

Publisher : Emerald Group Publishing

Page : 199 pages

File Size : 44,80 MB

Release : 2019-09-16

Category : Business & Economics

ISBN : 1789733790

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Book Description

There is a great deal of coverage on inequality, and the key determinants of recent trends are increasingly well-documented. However, much less is known about the driving forces behind international differences in inequality.



Bayesian Models for Categorical Data

Author : Peter Congdon

Publisher : John Wiley & Sons

Page : 446 pages

File Size : 33,91 MB

Release : 2005-12-13

Category : Mathematics

ISBN : 0470092386

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Book Description

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.



Finite Mixture and Markov Switching Models

Author : Sylvia Frühwirth-Schnatter

Publisher : Springer Science & Business Media

Page : 506 pages

File Size : 18,45 MB

Release : 2006-11-24

Category : Mathematics

ISBN : 0387357688

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Book Description

The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.