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Practical Smoothing
This user guide presents a popular smoothing tool with practical applications in machine learning, engineering, and statistics.
Statistical Modelling and Regression Structures
The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.
Seasonality in Human Mortality
Seasonal fluctuations in mortality are a persistent phenomenon, but variations from culture to culture pose fascinating questions. This book investigates whether sociodemographic and socioeconomic factors play a role as important for seasonal mortality as they do for mortality in general. Using modern statistical methods, the book shows, for example, that in the United States the fluctuations between winter and summer mortality are smaller the more years someone has spent in school.
Foundations of Intelligent Systems
This volume contains the papers selected for presentation at the 17th Inter- tional Symposium on Methodologies for Intelligent Systems (ISMIS 2008), held in York University, Toronto, Canada, May 21–23, 2008. ISMIS is a conference series started in 1986. Held twice every three years, ISMIS provides an inter- tional forum for exchanging scienti?c research and technological achievements in building intelligent systems. Its goal is to achieve a vibrant interchange - tween researchers and practitioners on fundamental and advanced issues related to intelligent systems. ISMIS 2008featureda selectionof latestresearchworkandapplicationsfrom the following areas related to intelligent systems: active...
Bayesian Methods
An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th
Generalized Additive Models for Location, Scale and Shape
This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields.
International Handbook of Philosophy of Education
This handbook presents a comprehensive introduction to the core areas of philosophy of education combined with an up-to-date selection of the central themes. It includes 95 newly commissioned articles that focus on and advance key arguments; each essay incorporates essential background material serving to clarify the history and logic of the relevant topic, examining the status quo of the discipline with respect to the topic, and discussing the possible futures of the field. The book provides a state-of-the-art overview of philosophy of education, covering a range of topics: Voices from the present and the past deals with 36 major figures that philosophers of education rely on; Schools of th...
Semiparametric Regression for the Social Sciences
An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
