Автор: Larry Wasserman Название: All of Nonparametric Statistics ISBN: 1441920447 ISBN-13(EAN): 9781441920447 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets.
Автор: Ghosal, Subhashis. Название: Fundamentals of Nonparametric Bayesian Inference ISBN: 0521878268 ISBN-13(EAN): 9780521878265 Издательство: Cambridge Academ Рейтинг: Цена: 12989.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.
Описание: 1 Nonparametric Statistics for the Biological Sciences1.1 Purpose of This Lesson 1.2 Data Types 1.2.1 Nominal Data 1.2.2 Ordinal Data1.2.3 Interval Data and Ratio Data1.3 Graphical Presentation of Populations1.3.1 Samples that Exhibit Normal Distribution1.3.2 Samples that Fail to Exhibit Normal Distribution1.4 R and Nonparametric Analyses1.4.1 Precision of Scales: Ordinal v Interval1.4.2 Deviation from Normal Distribution1.4.3 Sample Size: Number of Subjects for Each Breakout Group1.5 Definition of Nonparametric Analysis1.6 Statistical Tests and Graphics Associated with Distribution Patterns1.7 Prepare to Exit, Save, and Later Retrieve This R Session 2 Sign Test2.1 Background on This Lesson2.1.1 Description of the Data2.1.2 Null Hypothesis (Ho)2.2 Data Import of a .csv Spreadsheet-Type Data File into R2.3 Organize the Data and Display the Code Book2.4 Conduct a Visual Data Check2.5 Descriptive Analysis of the Data2.6 Conduct the Statistical Analysis2.7 Summary 2.8 Prepare to Exit, Save, and Later Retrieve This R Session 3 Chi-Square3.1 Background on This Lesson3.1.1 Description of the Data3.1.2 Null Hypothesis (Ho)3.2 Data Import of a .csv Spreadsheet-Type Data File into R3.3 Organize the Data and Display the Code Book3.4 Conduct a Visual Data Check3.5 Descriptive Analysis of the Data3.6 Conduct the Statistical Analysis3.7 Summary3.8 Addendum: Calculate the Chi-Square Statistic from Contingency Tables3.9 Prepare to Exit, Save, and Later Retrieve This R Session 4 Mann-Whitney U Test4.1 Background on This Lesson4.1.1 Description of the Data4.1.2 Null Hypothesis (Ho)4.2 Data Import of a .csv Spreadsheet-Type Data File into R4.3 Organize the Data and Display the Code Book4.4 Conduct a Visual Data Check4.5 Descriptive Analysis of the Data4.6 Conduct the Statistical Analysis4.7 Summary4.8 Addendum: Stacked Data v Unstacked Data4.9 Prepare to Exit, Save, and Later Retrieve This R Session 5 Wilcoxon Matched-Pairs Signed-Ranks Test5.1 Background on This Lesson5.1.1 Description of the Data5.1.2 Null Hypothesis (Ho)5.2 Data Import of a .csv Spreadsheet-Type Data File into R5.3 Organize the Data and Display the Code Book5.4 Conduct a Visual Data Check5.5 Descriptive Analysis of the Data5.6 Conduct the Statistical Analysis5.7 SummaryTest5.9 Addendum 2: Similar Functions from Different Packages5.10 Addendum 3: Nonparamteric v Parametric Confirmation of Outcomes5.11 Prepare to Exit, Save, and Later Retrieve This R Session 6 Kruskal-Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks6.1 Background on This Lesson6.1.1 Description of the Data6.1.2 Null Hypothesis (Ho)6.2 Data Import of a .csv Spreadsheet-Type Data File into R6.3 Organize the Data and Display the Code Book6.4 Conduct a Visual Data Check6.5 Descriptive Analysis of the Data6.6 Conduct the Statistical Analysis6.7 Summary6.8 Addendum: Comparison of Kruskal-Wallis Test Differences by Breakout Group6.9 Prepare to Exit, Save, and Later Retrieve This R Session 7 Friedman Two Way A
Автор: La Rocca Michele, Liseo Brunero, Salmaso Luigi Название: Nonparametric Statistics: 4th ISNPS, Salerno, Italy, June 2018 ISBN: 3030573087 ISBN-13(EAN): 9783030573089 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Preface.- Portfolio optimisation via graphical least squares estimation (Saeed Aldahmani, Hongsheng Dai, Qiao-Zhen Zhang and Marialuisa Restaino).- Change of Measure Applications in Nonparametric Statistics (Mayer Alvo).- Choosing between weekly and monthly volatility drivers within a Double Asymmetric GARCH-MIDAS model (Alessandra Amendola, Vincenzo Candila and Giampiero M. Gallo).- Goodness-of-fit test for the baseline hazard rate (Anfriani, A. and Butucea, C. and Gerardin E. and Jeantheau, T. and Lecleire U.).- Permutation tests for multivariate stratified data: synchronized or unsynchronized permutations? (Rosa Arboretti, Eleonora Carrozzo and Luigi Salmaso).- An extension of the dgLARS method to high-dimensional relative risk regression models (Luigi Augugliaro, Ernst C. Wit and Angelo M. Mineo).- A kernel goodness-of-fit test for maximum likelihood density estimates of normal mixtures. (Dimitrios Bagkavos and Prakash N. Patil)- Robust estimation of sparse signal with unknown sparsity cluster value (Eduard Belitser, Nurzhan Nurushev, and Paulo Serra).- Test for sign effect in intertemporal choice experiments: a nonparametric solution (Stefano Bonnini and Isabel Maria Parra Oller).- Nonparametric first-order analysis of spatial and spatio-temporal point processes (M.I. Borrajo, I. Fuentes-Santos and W. González-Manteiga).- Bayesian nonparametric prediction with multi-sample data (Federico Camerlenghi, Antonio Lijoi and Igor Prьnster).- Algorithm for Automatic Description of Historical Series of Forecast Error in Electrical Power Grid (Gaia Ceresa, Andrea Pitto, Diego Cirio and Nicolas Omont).- Linear wavelet estimation in regression with additive and multiplicative noise (Christophe Chesneau, Junke Kou and Fabien Navarro).- Speeding up algebraic-based sampling via permutations (Francesca Romana Crucinio and Roberto Fontana).- Obstacle Problems For Nonlocal Operators: A Brief Overview (Donatella Danielli, Arshak Petrosyan, and Camelia A. Pop).- Low and high resonance components restoration in multichannel data (Daniela De Canditiis and Italia De Feis).- Kernel circular deconvolution density estimation (Marco Di Marzio, Stefania Fensore, Agnese Panzera, Charles C. Taylor).- Asymptotic for Relative Frequency when Population is Driven by Arbitrary Unknown Evolution (Silvano Fiorin).- Semantic keywords clustering to optimize Text Ads campaigns (Pietro Fodra, Emmanuel Pasquet, Guillaume Mohr, Bruno Goutorbe, and Matthieu Cornec).- A Note on Robust Estimation of the Extremal Index (M. Ivette Gomes, Cristina Miranda and Manuela Souto de Miranda).- Multivariate permutation tests for ordered categorical data (Huiting Huang, Fortunato Pesarin, Rosa Arboretti, Riccardo Ceccato).- Smooth nonparametric survival analysis (Dimitrios Ioannides and Dimitrios Bagkavos).- Density estimation using multiscale local polynomial transforms (Maarten Jansen).- On Sensitivity of Metalearning: An Illustrative Study for Robust Regression (Jan Kalina).- Function-parametric empirical processes, projections and unitary operators (Estбte Khmaladze).- Rank-based Analysis of Multivariate Data in Factorial Designs and Its Implementation in R (Maximilian Kiefel and Arne C. Bathke).- Tests for Independence Involving Spherical Data (Pierre Lafaye de Micheaux, Simos Meintanis and Thomas Verdebout).- Interval-Wise Testing of Functional Data Defined on Two-dimensional Domains(Patrick B. Langthaler, Alessia Pini and Arne C. Bathke).- Assessing Data Support for the Simplifying Assumption in Bivariate Conditional Copulas (Evgeny Levi and Radu V. Craiu).- Semiparametric weighting estimations of a zero-inflated Poisson regression with missing in covariates (Lukusa, M.T. and Phoa, F.K.H.).- The Discrepancy Method for Extremal Index Estimation (Natalia Markovich).- Correction for optimisation bias in structured sparse high-dimensional variable selection (Bastien Marquis and Maarten Jansen).- United Statistical Algorithms and Data Science: An Introd
Автор: Cao Ricardo, Gonzбlez Manteiga Wenceslao, Romo Juan Название: Nonparametric Statistics: 2nd Isnps, Cбdiz, June 2014 ISBN: 3319823884 ISBN-13(EAN): 9783319823881 Издательство: Springer Рейтинг: Цена: 26194.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: S. Chakraborty and S. Datta: Robust Estimation in AFT Models and a Covariate Adjusted Mann-Whitney Statistic for Comparing Two Sojourn Times.- G. Benini, S. Sperlich and R. Theler: Varying Coefficient Models Revisited: An Econometric View.- J. Hidalgo and V. Dalla: Testing for Breaks in Regression Models with Dependent Data.- D. Bagkavos, P. N. Patil and A. T. A. Wood: A Numerical Study of the Power Function of a New Symmetry Test.- N. Markovich: Nonparametric Estimation of Heavy-Tailed Density by the Discrepancy Method.- S. Hudecova, M. Huskova and S. Meintanis: Change Detection in INARCH Time Series of Counts.- M. P. Espinosay, E. Ferreiraz and W. Stute: Discrimination, Binomials and Glass Ceiling Effects.- A. Antoniadis, X. Brossat, Y. Goude, J.-M. Poggi and V. Thouvenot: Automatic Component Selection in Additive Modeling of French National Electricity Load Forecasting.- S. Bonnini: Nonparametric Test on Process Capability.- E. Boj and T. Costa: Claim Reserving using Distance-Based Generalized Linear Models.- A. V. Dobrovidov: Regularization of Positive Signal Nonparametric Filtering in Multiplicative Observation Model.- V. Patrangenaru, K. D. Yao and R. Guo: Extrinsic Means and Antimeans.- G. Koshkin and V. Smagin: Kalman Filtering and Forecasting Algorithms with Use of Nonparametric Functional Estimators.- A. Meneses, S. Naya, I. Lopez-de-Ullibarri and J. Tarro-Saavedra: Nonparametric Method for Estimating the Distribution of Time to Failure of Engineering Materials.- G. J. Szekely and M. L. Rizzo: Partial Distance Correlation.
Автор: Corder Название: Nonparametric Statistics - A Step-by-Step Approach 2e ISBN: 1118840313 ISBN-13(EAN): 9781118840313 Издательство: Wiley Рейтинг: Цена: 13139.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught.
Автор: Kolassa, John E. Название: An Introduction to nonparametric statistics ISBN: 0367194848 ISBN-13(EAN): 9780367194840 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.
Автор: Kraska-MIller, M. Название: Nonparametric Statistics for Social and Behavioral Sciences ISBN: 0367379104 ISBN-13(EAN): 9780367379100 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM's SPSS software.
This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text:
Explains a conceptual framework for each statistical procedure
Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure
Details SPSS paths for conducting various analyses
Discusses the interpretations of statistical results and conclusions of the research
With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.
Автор: Groeneboom Название: Nonparametric Estimation under Shape Constraints ISBN: 0521864011 ISBN-13(EAN): 9780521864015 Издательство: Cambridge Academ Рейтинг: Цена: 11880.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.
Автор: Wasserman Название: All of Nonparametric Statistics ISBN: 0387251456 ISBN-13(EAN): 9780387251455 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets.
Автор: Mayer Alvo; Philip L. H. Yu Название: A Parametric Approach to Nonparametric Statistics ISBN: 3030068048 ISBN-13(EAN): 9783030068042 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.
Автор: Bhattacharya Название: Nonparametric Inference on Manifolds ISBN: 1107484316 ISBN-13(EAN): 9781107484313 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ideal for statisticians, this book will also interest probabilists, mathematicians, computer scientists, and morphometricians with mathematical training. It presents a systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important applications in medical diagnostics, image analysis and machine vision.
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