Suchergebnisse

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods

"Provides a unified approach to social research, integrating both agent-based models and behavioral studies. Introduces the reader to all the concepts, tools and references that are required for conducting research in behavioral computational social science"--

Frontmatter -- Overview of Multivariate and Regression Methods -- The Seven Habits of Highly Effective Quants: A Review of Elementary Statistics Using Matrix Algebra -- Fundamentals of Matrix Algebra -- Factor Analysis and Related Methods: Quintessentially Multivariate -- Multivariate Graphics -- Canonical Correlation: The Underused Method -- Hotelling's as the Simplest Case of Multivariate Inference -- Multivariate Analysis of Variance -- Multiple Regression and the General Linear Model -- Appendices: Statistical Tables -- Name Index -- Subject Index.

An effective technique for data analysis in the social sciencesThe recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings a

Introduction : distributions and inference for categorical data -- Describing contingency tables -- Inference for contingency tables -- Introduction to generalized linear models -- Logistic regression -- Building and applying logistic regression models -- Logit models for multinomial responses -- Loglinear models for contingency tables -- Building and extending loglinear/logit models -- Models for matched pairs -- Analyzing repeated categorical response data -- Random effects : generalized linear mixed models for categorical responses -- Other mixture models for categorical data -- Asymptotic theory for parametric models -- Alternative estimation theory for parametric models -- Historical tour of categorical data analysis.

Applied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 2 Introduction to Regression; 3 Introduction to Smoothing; 4 Bivariate Distributions; 5 Two-Dimensional Plots; PART II. TOOLS; 6 Simple Linear Regression; 7 Introduction to Multiple Linear Regression; 8 Three-Dimensional Plots; 9 Weights and Lack-of-Fit; 10 Understanding Coefficients; 11 Relating Mean Functions; 12 Factors and Interactions; 13 Response Transformations; 14 Diagnostics I: Curvature and Nonconstant Variance; 15 Diagnostics II: Influence and Outliers.

"Forces shaping human history are complex, but the course of history is undeniably changed on many occasions by conscious acts. These may be premeditated or responsive, calmly calculated or performed under great pressure. They may also be considered to be successful or catastrophic, but how are historians to make such judgments and appeal to evidence in support of their conclusions? Further, and crucially, how exactly are we to distinguish probable unrealized alternatives from improbable ones? This book describes some of the modern statistical techniques that can begin to answer this question, as well as some of the difficulties in doing so. Using simple, well-quantified cases drawn from military history, we claim that statistics can now help us to navigate the near-truths, the envelope around the events with which any meaningful historical analysis must deal, and to quantify the basis of such analysis. Quantifying Counterfactual Military History is intended for a general audience who are interested in learning more about statistical methods both in military history and for wider applications"--

Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; 1: Introduction; I: Methodology for Statistical Analysis of Environmental Processes; 2: Modeling for environmental and ecological processes; 2.1 Introduction; 2.2 Stochastic modeling; 2.3 Basics of Bayesian inference; 2.3.1 Priors; 2.3.2 Posterior inference; 2.3.3 Bayesian computation; 2.4 Hierarchical modeling; 2.4.1 Introducing uncertainty; 2.4.2 Random effects and missing data; 2.5 Latent variables; 2.6 Mixture models; 2.7 Random effects; 2.8 Dynamic models; 2.9 Model adequacy; 2.10 Model comparison

Front Cover; Computational Probability; Copyright Page; Table of Contents; Contributors; Preface; Chapter 1. Some Ideas in Computational Probability; References; Chapter 2. Computational Problems Related to the Galton- Watson Press; I. THE PROBABILITY DISTRIBUTIONS OF PHASE TYPE; 2. THE SUCCESSIVE GENERATION SIZES IN A GALTON-WATSON PROCESS; 3. A GALTON-WATSON PROCESS EMBEDDED INTHE MX /G/1 QUEUE; 4. THE MACIMUM GENERATION SIZE BEFORE EXTINCTION; 5. THE PROBABILITY OF EVENTUAL ABSORPTION; REFERENCES; Chapter 3. Central Limit Analogues for Markov Population Processes; 1. SUMMARY.

Introduction -- Contingency tables -- Generalized linear models -- Logistic regression -- Building and applying logistic regression models -- Multicategory logit models -- Loglinear models for contingency tables -- Models for matched pairs -- Modeling correlated, clustered responses -- Random effects : generalized linear mixed models -- A historical tour of categorical data analysis -- Appendix A. Software for categorical data analysis -- Appendix B. Chi-squared distribution values.

With the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists, this textbook equips social scientists with all concepts, explanations, and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to: -Identify and access place-based longitudinal and cross-sectional data sources and formats-Conduct advanced data management, including crosswalks, joining, and matching -Fully connect social network analyses with geospatial statistics-Formulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicators-Estimate distance measures across units that follow road network paths -Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilities-Follow best practices for presenting spatial analyses, findings, and implications-Master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methods-Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors-Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models, instrumental variables, and difference in differences-Expand the SSEM analyses to connections that emerge via social interactions, such as co-authorship and advice networks, or any form of relational data The applied nature of the book along with the cost-free, multi-operative R software makes the usability and applicability of this textbook worldwide