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Robust Statistics, Data Analysis, and Computer Intensive Methods

sity's Brass Ensemble. The subsequent Workshop Robust Statistics, Data Analysis and Computer Intensive Methods in Schloss Thurnau, Friday until;

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Robust Correlation Theory & Aplications

applications of robust correlation methods to exploratory data analysis, multivariate statistics, statistics of time series, and to real-life data;

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Robust Statistics

. * Covers regression methods for data mining applications. * Includes examples with real data and applications using the S-Plus robust statistics;

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Robust Statistics

regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust;

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Applied Nonparametric Statistical Methods

latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with;

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Computationally Intensive Statistics for Intelligent IoT

of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design;

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Elements of Statistical Computing

statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating;

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The Analysis of Biological Data

-analysis and computer-intensive methods. Instructors and students consistently praise the book's clear and engaging writing, strong visualization;

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Computer Intensive Methods in Statistics

Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and;

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Computer Intensive Methods in Statistics

Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and;

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Recent Advances in Robust Statistics: Theory and Applications

interested in robust statistics, data analysis and related areas. The conference is meant for theoretical and applied statisticians, data analysts;

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Robust Methods in Biostatistics

emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health;

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Modern Industrial Statistics - With Applications in R, MINITAB and JMP, 3e

process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on;

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Empirical Methods for Artificial Intelligence

designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Computer science and artificial;

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Robust Multivariate Analysis

by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor;

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Introduction To Computer-Intensive Methods Of Data Analysis In Biology

This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological;

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Introduction To Computer-Intensive Methods Of Data Analysis

This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological;

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Statistics at Square Two 3e

, including the different types of data, statistical models, and computer-intensive methods Comprehensive explorations of multiple linear regression;

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Statistics for Data Scientists

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science;

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Methodology in Robust and Nonparametric Statistics

are presently nourished by data mining and high-level computer-based algorithms, but to work actively with robust and nonparametric procedures;

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Methodology in Robust and Nonparametric Statistics

are presently nourished by data mining and high-level computer-based algorithms, but to work actively with robust and nonparametric procedures;

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Exploratory Data Analysis in Business and Economics

. This textbook, Exploratory Data Analysis in Business and Economics, aims to familiarise students of economics and business as well as;

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Principles and Methods for Data Science

this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining;

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Data Analysis and Statistics for Geography, Environmental Science, and Engineering

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental;

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Statistics for Business

More Effective in Schools of Business (MSMESB). It emphasizes modern statistical methods and data analysis with a decreased emphasis on;

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Data Analysis and Applications 4

from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a;

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Robust Methods for Data Reduction

part focuses on cluster and discriminant analysis. The authors explain how to perform sample reduction by finding groups in the data;

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