Using Science to Improve the BLM Wild Horse and Burro Program: A Way Forward reviews the science that underpins the Bureau of Land Management's oversight of free-ranging horses and burros on federal public lands in the western United States, concluding that constructive changes could be implemented. The Wild Horse and Burro Program has not used scientifically rigorous methods to estimate the population sizes of horses and burros, to model the effects of management actions on the animals, or to assess the availability and use of forage on rangelands. Evidence suggests that horse populations are growing by 15 to 20 percent each year, a level that is unsustainable for maintaining healthy horse populations as well as healthy ecosystems. Promising fertility-control methods are available to help limit this population growth, however. In addition, science-based methods exist for improving population estimates, predicting the effects of management practices in order to maintain genetically diverse, healthy populations, and estimating the productivity of rangelands. Greater transparency in how science-based methods are used to inform management decisions may help increase public confidence in the Wild Horse and Burro Program.

This book takes an easy-to-understand look at the statistical approach called the person-centered method. Instead of analyzing means, variances and covariances of scale scores as in the common variable-centered approach, the person-centered approach analyzes persons or objects grouped according to their characteristic patterns or configurations in contingency tables. The main focus of the book will be on Configural Frequency Analysis (CFA; Lienert and Krauth, 1975) which is a statistical method that looks for over and under-frequented cells or patterns. Over frequented means that the observations in this cell or configuration are observed more often than expected, under-frequented means that this cell or configuration is observed less often than expected. In CFA a pattern or configuration that contains more observed cases than expected is called a type; similarly, a pattern or configuration that is less observed than expected are called an antitype. CFA is similar to log-linear modeling. In log-linear modeling the goal is to come up with a fitting model including all important variables. Instead of fitting a model, CFA looks at the significant residuals of a log-linear model. The book describes the use of an R-package called confreq (derived from Configural Frequency Analysis). The use of the software package is described and demonstrated with data examples.

This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited dependent variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.The book provides br

Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples

Model organisms represent an invaluable resource for fundamental and applied research, allowing the identification of the mechanistic basis of evolutionary innovations. This article collection will showcase studies of established as well as emerging Model Organisms in Plant Developmental Biology - their effectiveness and limitations, that have significance to the field broadly, including EvoDevo. Classically used for genetic and molecular studies in Plant Biology, model organisms are progressively entering many subdisciplines within Plant Development and EvoDevo. Recent advancements in the fast-growing field of plant model organisms, and their hugely increased phylogenetic breadth and availability of genomes and transgenic techniques, have led to a burst of innovative ideas and synthesis in recent publications spanning the range from an analysis of fossils to single-cell sequencing. However, it also raises the question of how broad is the application of knowledge gained from these studies, and its relevance to the field of Plant Development and EvoDevo. To address those questions, this research topic focuses on new insights, latest discoveries, current challenges, and future perspectives in the study of model organisms and how much knowledge gained from them can be extrapolated broadly. Authors are encouraged to identify the greatest unifying concepts in their sub-disciplines, and the challenges, emerging from the use of model plants, as well as to put forward potential solutions to address those challenges.

Completely devoted to applicati on of models to opti mize the use of limited water and nutrients in various climates, this collecti on will inspire confi dence in the capacity of modeling to tackle the biggest threats to secure agriculture. To obtain the most producti on from available water while maintaining natural resources, we need whole system–based quanti tati ve knowledge and tools to help select appropriate crops and manage water and associated inputs on a site-specifi c basis under changing climate. Site-specifi c experimental results are available for limited locati ons, limited periods of ti me, and limited management opti ons. Well-tested process models of cropping systems can extend fi eld research results to long-term weather conditi ons, as well as other climates and soils, allowing us to explore new management opti ons. The case studies in this volume are promising examples of these kinds of soluti ons.

This book includes a collection of articles that present recent developments in the fields of optimization and dynamic game theory, economic dynamics, dynamic theory of the firm, and population dynamics and non standard applications of optimal control theory. The authors of the articles are well respected authorities in their fields and are known for their high quality research in the fields of optimization and economic dynamics.

This book explores the view that normative behaviour is part of a complex of social mechanisms, processes and narratives that are constantly shifting. From this perspective, norms are not a kind of self-contained social object or fact, but rather an interplay of many things that we label as norms when we ‘take a snapshot’ of them at a particular instant. Further, this book pursues the hypothesis that considering the dynamic aspects of these phenomena sheds new light on them. The sort of issues that this perspective opens to exploration include: Of what is this complex we call a "social norm" composed of? How do new social norms emerge and what kind of circumstances might facilitate such an appearance? How context-specific are the norms and patterns of normative behaviour that arise? How do the cognitive and the social aspects of norms interact over time? How do expectations, beliefs and individual rationality interact with social norm complexes to effect behaviour? How does our social embeddedness relate to social constraint upon behaviour? How might the socio-cognitive complexes that we call norms be usefully researched?