Presents familiar elements of estimation theory from an analog perspective discussing recent developments in the theory of analog estimation and new results that offer flexibility in empirical research. Annotation copyrighted by Book News, Inc., Portland, OR

Entropy, mutual information and divergence measure the randomness, dependence and dissimilarity, respectively, of random objects. In addition to their prominent role in information theory, they have found numerous applications, among others, in probability theory statistics, physics, chemistry, molecular biology, ecology, bioinformatics, neuroscience, machine learning, linguistics, and finance. Many of these applications require a universal estimate of information measures which does not assume knowledge of the statistical properties of the observed data. Over the past few decades, several nonparametric algorithms have been proposed to estimate information measures. Universal Estimation of Information Measures for Analog Sources presents a comprehensive survey of universal estimation of information measures for memoryless analog (real-valued or real vector-valued) sources with an emphasis on the estimation of mutual information and divergence and their applications. The book reviews the consistency of the universal algorithms and the corresponding sufficient conditions as well as their speed of convergence. Universal Estimation of Information Measures for Analog Sources provides a comprehensive review of an increasingly important topic in Information Theory. It will be of interest to students, practitioners and researchers working in Information Theory

This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.

Project estimating plays a vital role in project management. Typically completed in the initial planning stages, accurate project estimation can be a difficult task. Organizations and project managers should use these initial estimates to baseline the project schedule and cost, then refine these estimates as the project develops. Accurate estimation and refinement of the estimates leads to better and earlier decision making, thus maximizing value.Developed within the framework of A Guide to the Project Management Body of Knowledge (PMBOK&® Guide) &– Sixth Edition and other PMI standards, the Practice Standard for Project Estimating &– Second Edition focuses on providing models for the project management profession in both plan-driven and change-driven adaptive (agile) life cycles. This practice standard describes the aspects of project estimating that are recognized as good practice on most projects most of the time and that are widely recognized and consistently applied.PMI practice standards describe processes, activities, constraints, inputs, and outputs for specific discipline subject areas and are targeted to all practitioners within projectized organizations, not just project managers.

Building on the success of the first edition, this popular text book has now been updated and revised. Covering both analog and digital signal processing techniques in an evenly balanced manner, Professor Baher provides an excellent introductory and comprehensive text emphasising how analog and digital techniques complement each other rather than compete. Brings the entire area of signal processing within the scope of modern undergraduate curricula Discusses topics such as spectral analysis of continuous and discrete signals (deterministic and random), Fourier, Laplace, and z-transforms, analysis of continuous and discrete systems and circuits, design of analog and digital filters, fast Fourier transform algorithms and finite word-length effects in digital processors Presents a final chapter on advanced signal processing (including linear estimation, adaptive filters, over-sampling sigma-delta converters, and wavelets) to encourage further interest Contains numerous solved examples throughout and MATLAB(r) exercises at the end of each chapter Written primarily for undergraduates, Analog Digital Signal Processing will also be an authoritative text for postgraduate students and professional engineers.

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.