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Empirical Process Techniques for Dependent Data

of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields;

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Weak Convergence of Stochastic Processes

in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad;

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Inference for Heavy-Tailed Data

in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are;

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Chemical Process Performance Evaluation

and process upset. The book concludes with an extensive discussion on the use of data analysis techniques for the special case of web and;

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Guide To Modern Econometrics

and empirical, reviewing key concepts. Updated and expanded coverage, on various topics such as missing data, outliers, forecast evaluation;

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A Guide to Modern Econometrics

focusing on what is important for doing and understanding empirical work. It serves as a guide to alternative techniques with the emphasis on the;

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Statistical Modeling for Management

bank of downloadable data sets to compliment the tables provided in the textbook are provided free for you.;

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Econometrics

concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification;

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Empirical Research in Software Engineering

, on data collected from source code repositories. She also covers the application of machine learning techniques in empirical software;

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A Companion to Econometric Analysis of Panel Data

effects, as well as Hausman's test for correlated effects. It also provides extensions of panel data techniques to serial correlation, spatial;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov;

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Power Plant Surveillance and Diagnostics

computer-assisted plant surveillance and diagnosis.- Empirical and analytical methods for on-line calibration monitoring and data reconciliation;

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Measurement Error in Nonlinear Models

, affects the quality of the data and limits its usefulness for empirical research. Generally, a disclosure limitation method can be regarded as a;

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Data Analysis

Written at a level appropriate for the advanced undergraduate course on data analysis, this accessible volume introduces the reader to the;

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Empirical Research for Software Security

is based on shared expert knowledge, claims, and opinions. Empirical methods, including data analytics, allow extracting knowledge and insights;

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Process Modelling and Model Analysis

techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform;

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Fundamentals of Business Intelligence

and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business;

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Quantitative Techniques for Competition and Antitrust Analysis

This book combines practical guidance and theoretical background for analysts using empirical techniques in competition and antitrust;

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Artificial Intelligence in Medical Sciences and Psychology

is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records;

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Resampling Methods for Dependent Data

By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical;

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Qualitative Research and Theory Development

Empirical data is one of the cornerstones of knowledge in the social sciences, and yet the researcher often takes it for granted, reserving;

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Data Analysis Using Stata 3rd Ed

interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent;

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Econometric Analysis of Panal Data

techniques from a key author in this field. Updated topics include dynamic panels, limited dependent variable panel data models, spatial panels, GMM;

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Test Case Prioritization Based on Data Reuse

for each test case; the better sequences of tests are those that reuse more data. We applied these techniques in a mobile phone testing;

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Statistical Learning for Big Dependent Data

Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting;

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Analysing Economic Data

Covers the key issues required for students wishing to understand and analyse the core empirical issues in economics. It focuses on;

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Techniques for Exploiting Unlabeled Data

process it mainly due to technological advances that allow us to store and process more data than ever before. Thus the question of developing;

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