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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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Resampling Methods

This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and;

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Mathematical Statistics with Resampling and R

. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis;

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Financial Data Resampling for Machine Learning Based Trading

Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as;

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Practitioner'S Guide To Resampling For Data Analysis, Data M

. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the;

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A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling

. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the;

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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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An Introduction to Bootstrap Methods with Applications to R

reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding;

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Empirical Methods for Artificial Intelligence

This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment;

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Recent Advances and Trends in Nonparametric Statistics

nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting;

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Analysis of Survival Data with Dependent Censoring

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily;

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Introductory Statistics and Analytics

provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic;

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Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates

This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent;

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Analyzing the Large Number of Variables in Biomedical and Satellite Imagery

multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis;

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Nonparametric Function Estimation, Modeling, and Simulation

analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling;

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Data Mining with SPSS Modeler

resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.;

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Statistical Models and Methods for Lifetime Data

Praise for the First Edition An indispensable addition to any serious collection on lifetime data analysis and ...a valuable contribution;

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Statistical Thinking for Managers

Focusing on the analysis of data using modern statistical and spreadsheet software, Hildebrand and Ott emphasize making sense of data and;

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Monte Carlo Simulation and Resampling Methods for Social Science

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo sim;...

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Vibration-Based Condition Monitoring of Wind Turbines

signal envelope, used for condition monitoring of drivetrains. A special emphasis is placed on wind turbines and on the fact that they work;

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Computational Methods for Data Evaluation and Assimilation

then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data;

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Computational Methods for Data Evaluation and Assimilation

then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data;

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U Statistics Mm Estimators and Resampling

This is an introductory text on a broad class of statistical estimators that are minimizers of conve;...

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Analysis Of Longitudinal Data

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis;

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Computational Statistics in the Earth Sciences

ocean sciences, this book is ideal for those who want to learn how to analyse data using MATLAB in a statistically-rigorous manner.;

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Statistics is Easy!

distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot;

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Statistics Is Easy!

distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot;

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