Last edited by Faushakar
Saturday, April 18, 2020 | History

5 edition of Statistical methods for fuzzy data found in the catalog.

Statistical methods for fuzzy data

  • 180 Want to read
  • 28 Currently reading

Published by Wiley in Hoboken, N.J .
Written in English


Edition Notes

Includes bibliographical references and index.

StatementReinhard Viertl
Classifications
LC ClassificationsQA312.5 .V54 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL24524174M
ISBN 109780470699454
LC Control Number2010031105
OCLC/WorldCa246887089


Share this book
You might also like
undesirable element

undesirable element

Real World Math

Real World Math

Bucket-Drilling the Coso Mercury Deposit Inyo County, Calif.

Bucket-Drilling the Coso Mercury Deposit Inyo County, Calif.

Calculost?

Calculost?

Clean Development Mechanism

Clean Development Mechanism

Intra-urban migration of Mexico City poor

Intra-urban migration of Mexico City poor

Tigers in New Zealand?

Tigers in New Zealand?

Kashmir

Kashmir

Elizabeth Goudge reader

Elizabeth Goudge reader

The Wear Valley way

The Wear Valley way

Forgotten fortunes.

Forgotten fortunes.

short history of our own times, from the accession of Queen Victoria to the general election of 1880

short history of our own times, from the accession of Queen Victoria to the general election of 1880

Dictionary of civics and government

Dictionary of civics and government

Statistical methods for fuzzy data by R. Viertl Download PDF EPUB FB2

In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information.

Key Features:Cited by: In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information.

In this Statistical methods for fuzzy data book, the foundations of the Statistical methods for fuzzy data book of fuzzy data Statistical methods for fuzzy data book explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information.

Key Features:4/5(1). In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then Statistical methods for fuzzy data book to the analysis of fuzzy data and fuzzy a-priori information.

Statistical Methods for Fuzzy Data by Reinhard Viertl Get Statistical Methods for Fuzzy Data now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from + publishers.

In this book, the foundations of the description of fuzzy data are explained, including methods Statistical methods for fuzzy data book how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori : Part IV CLASSICAL STATISTICAL INFERENCE FOR FUZZY DATA.

Generalized Point Estimations. Estimations based on Fuzzy Samples. Sample Moments. Problems. Generalized Confidence Regions. Confidence Functions. Fuzzy Confidence Regions. Problems. Statistical Tests for Fuzzy Data. Test Statistics and Fuzzy Data. Viertl R. () Statistics with Fuzzy Data.

In: Della Riccia G., Kruse R., Viertl R. (eds) Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence.

International Centre for Mechanical Sciences (Courses and Lectures), vol Cited by: 4. Statistical methods for fuzzy data book fuzzy approach to statistical analysis Article (PDF Available) in Computational Statistics & Data Analysis 51(1) November with 2, Reads How we measure 'reads'.

Descriptive methods of time series analysis work without stochastic models. The goal of these methods is to find trends and seasonal influence in time series by elementary methods. A short survey of such methods for standard time series is given in Janacek ().

A fuzzy time series (x t *) t ∈ T is an ordered sequence of fuzzy numbers, where usually T = {1, 2,N}. Statistical Methods for Fuzzy Data An area of increasing importance with applications in areas such as environmental stats and social science Provides basic Statistical procedures for fuzzy data as well as Bayesian inference procedures with fuzzy information Authoritative and expert author Unifies the currently dispersed literature in the subject.

An interdisciplinary framework for learning methodologies―covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from by: Thus there is a need for fuzzy data analysis. to write a book ab out the topic of fuzzy data analysis for Reidel The second approach tries to analyze fuzzy data by using statistical methods.

The R package SAFD Statistical methods for fuzzy data book Analysis of Fuzzy Data) provides basic tools for elementary statistics with one dimensional Fuzzy Data (in the form of polygonal fuzzy numbers).

In particular, the Author: Takehiko Nakama. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation.

The last three chapters then develop more advanced statistical ideas, focusing on interval estimation, characteristic functions, and correcting Cited by: A Handbook of Statistics. Introduction to Vectors. Decision-Making using Financial Ratios. Statistics for Business and Economics.

Understanding Statistics. An Introduction to Matlab. A Refresher Course in Mathematics. Introduction to statistical data analysis with R. Elementary Linear Algebra: Part I. Introduction to Probability. Matrix Algebra. The development of fuzzy approaches to classify ‘crisp’ data started soon after the formalization of fuzzy sets (Zadeh ).In fact, Zadeh along with Bellman and Kalaba were the first in suggesting fuzzy sets as a theoretical basis to develop clustering algorithms (Bellman et al.

).Some of the most influential pioneer works on the subject are, among others, those by Ruspini ( Cited by: 2. Statistical Methods for Fuzzy Data by Reinhard Viertl Get Statistical Methods for Fuzzy Data now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from + publishers. Start your free trial. In our group we work on data analysis and image analysis with fuzzy clustering methods. A cluster analysis is a method of data reduction that tries to group given data into clusters.

Data of the same cluster should be similar or homogenous, data of disjunct clusters should be maximally different. Doring C, Lesot MJ, Kruse R () Data analysis with fuzzy clustering methods. Comput Stat Data Anal – MathSciNet Google Scholar Dunn JC () A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.

The method of handling fuzziness upon the usual method of statistical hypotheses testing is proposed. We transact the h-level set of fuzzy data for the sake of invoking usual method of statistical hypotheses propose the decision rules that are used to accept or reject the null and alternative hypotheses with the notions of degrees of optimism and pessimism by solving optimization Cited by: The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context.

Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning, and models in bigCited by: Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy.

Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be by: TY - JOUR AU - Mareš, Milan TI - Fuzzy data in statistics JO - Kybernetika PY - PB - Institute of Information Theory and Automation AS CR VL - 43 IS - 4 SP - EP - AB - The development of effective methods of data processing belongs to important challenges of modern applied mathematics and theoretical information science.

If the natural uncertainty of the data means their Cited by: 3. Statistical quality control (SQC) is an important field where both theory of probability and theory of fuzzy sets may be used. In the paper we give a short overview of basic problems of SQC that have been solved using both these theories simultaneously.

Some new results on the applications of fuzzy sets in SQC are presented in details. We also present problems which are still open, and whose Cited by: Statistical methods for big data: A scenic tour. Chapter Tibshirani and Friedman, 2nd edition ), a popular reference book for statistics and machine learning researchers.

An Introduction. Theories of fuzzy logic and of random closed sets are used as basic ingredients in building statistical concepts and procedures in the context of imprecise data, including coarse data analysis. The monograph also aims at motivating statisticians to look at fuzzy statistics to enlarge the domain of applicability of statistics in general.

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data.

Rather, the impossibility that fuzzy sets and fuzzy logic could make any sense or have any usefulness *at all* was posited. Ina top statistics journal did not find it necessary to edit a.

fm JWBKNaes Printer: Yet to Come Contents Preface ix Acknowledgements xi 1 Introduction 1 The Distinction between Trained Sensory Panels and Consumer Panels 1 The Need for Statistics in Experimental Planning and Analysis 2 Scales and Data Types 3 Organisation of the Book 3File Size: 2MB.

Data pre-processing If, for example, in quality control some acoustic signals have to be investigated, it W. Meier et al. / Fuzzy data analysis - Methods and industrial applications 21 becomes necessary to filter these data in order to overcome the problems of noisy input (see also Chapter ).Cited by: His other publications include more than refereed journal articles and book chapters.

His primary research interests are in judgment and decision making under ignorance and uncertainty, statistical methods for the social sciences, and applications of fuzzy set theory to the social by: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications.

Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). Read the latest articles of Computational Statistics & Data Analysis atElsevier’s leading platform of peer-reviewed scholarly literature.

Statistical Methods to Determine Variables Based on Fuzzy Data. Regression analysis is a statistical technique to arrive at the best mathematical expression that can correctly determine the relationship between independent variables depending upon a response.

The parameters of the regression are determined by minimizing the difference between the real value and observed data, which may have. statistics but instead to find practical methods for analyzing data, a strong emphasis has been put on choice of appropriate standard statistical model and statistical inference methods (parametric, non-parametric, resampling methods) for different types of data.

Then, methods for processing multivariate data are briefly reviewed. The. Book Abstract: An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data.

It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that. 12 Fuzzy Data Bases and Queries Introduction Fuzzy Relational Databases Fuzzy Queries in Crisp Databases 13 Fuzzy Data Analysis Introduction Methods for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based Approaches Neural Net Approaches This book is essentially about learning R with an emphasis on Epicalc.

Readers should have some background in basic computer usage. With R, Epicalc and the supplied datasets, the users should be able to go through each lesson learning the concepts of data management, related statistical theories and the practice of data analysis and powerful.

Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods.

In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to Cited by: 4. Statistical Pdf Handbook A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools 2 Statistical data 37 The Statistical Method 53 Misuse, Misinterpretation and Bias 60 basis of such understanding, is the primary function of modern statistical Size: 1MB.