%{search_type} search results

• The Evidence is the Evidence
• The Evidential Approach
• Two Samples
• ANOVA
• Correlation and Regression
• Categorical Data
• Nonparametric Analyses
• Other Useful Techniques
• Orthogonal Polynomials
• Occam's Bonus
• Problems with p Values.

• Part I Statistical Prediction.- 1 Theory of Statistical Prediction.- Part II Unbiased Estimation.- 2 Unbiased Estimation in Case of the Class of Distributions of Finite Rank.- 3 Some Theorems on Invariant Estimators of Location.- Part III Robust Estimation.- 4 Robust Estimation and Robust Parameter.- 5 Robust Estimation of Location in the Case of Measurement of Physical Quantity.- 6 A Uniformly Asymptotically Efficient Estimator of a Location Parameter.- Part IV Randomization.- 7 Theory of Randomized Designs.- 8 Some Remarks on General Theory for Unbiased Estimation of a Real Parameter of a Finite Population.- Part V Tests of Normality.- 9 The Studentized Empirical Characteristic Function and Its Application to Test for the Shape of Distribution.- 10 Tests of Univariate Normality.- 11 The Tests for Multivariate Normality.- Part VI Model Selection.- 12 On the Problem of Model Selection Based on the Data.-Part VII Asymptotic Approximation.- 13 On Sum of 0-1 Random Variables (I. Univariate Case).- 14 On Sum of 0-1 Random Variables (II. Multivariate Case).- 15 Algebraic Properties and Validity of Univariate and Multivariate Cornish-Fisher Expansion.
• (source: Nielsen Book Data)

This volume is a reorganized edition of Kei Takeuchi's works on various problems in mathematical statistics based on papers and monographs written since the 1960s on several topics in mathematical statistics and published in various journals in English and in Japanese. They are organized into seven parts, each of which is concerned with specific topics and edited to make a consistent thesis. Sometimes expository chapters have been added. The topics included are as follows: theory of statistical prediction from a non-Bayesian viewpoint and analogous to the classical theory of statistical inference; theory of robust estimation, concepts, and procedures, and its implications for practical applications; theory of location and scale covariant/invariant estimations with derivation of explicit forms in various cases; theory of selection and testing of parametric models and a comprehensive approach including the derivation of the Akaike's Information Criterion (AIC); theory of randomized designs, comparisons of random and conditional approaches, and of randomized and non-randomized designs, with random sampling from finite populations considered as a special case of randomized designs and with some separate independent papers included. Theory of asymptotically optimal and higher-order optimal estimators are not included, since most of them already have been published in the Joint Collected Papers of M. Akahira and K. Takeuchi. There are some topics that are not necessarily new, do not seem to have attracted many theoretical statisticians, and do not appear to have been systematically dealt with in textbooks or expository monographs. One purpose of this volume is to give a comprehensive view of such problems as well.
(source: Nielsen Book Data)

• Intro
• Prefazione
• Indice
• Parte I Statistica inferenziale
• 1 Fondamenti di probabilità e statistica
• 1.1 Richiami di teoria
• 1.2 Esercizi
• 1.3 Soluzioni
• 2 Statistiche sufficienti, minimali e complete
• 2.1 Richiami di teoria
• 2.2 Esercizi
• 2.3 Soluzioni
• 3 Stimatori puntuali
• 3.1 Richiami di teoria
• 3.2 Esercizi
• 3.3 Soluzioni
• 4 Uniform Minimum Variance Unbiased Estimators (UMVUE)
• 4.1 Richiami di teoria
• 4.2 Esercizi
• 4.3 Soluzioni
• 5 Likelihood Ratio Test
• 5.1 Richiami di teoria
• 5.2 Esercizi
• 5.3 Soluzioni
• 6 Test uniformemente più potente

• 6.1 Richiami di teoria
• 6.2 Esercizi
• 6.3 Soluzioni
• 7 Intervalli di confidenza
• 7.1 Richiami di teoria
• 7.2 Esercizi
• 7.3 Soluzioni
• 8 Statistica asintotica
• 8.1 Richiami di teoria
• 8.2 Esercizi
• 8.3 Soluzioni
• Parte II Modelli di regressione e analisi della varianza
• 9 Regressione lineare
• 9.1 Richiami di teoria
• 9.2 Esercizi
• 9.3 Soluzioni
• 10 Modelli lineari generalizzati
• 10.1 Richiami di teoria
• 10.2 Esercizi
• 10.3 Soluzioni
• 11 ANOVA: analisi della varianza
• 11.1 Richiami di teoria
• 11.2 Esercizi
• 11.3 Soluzioni
• 12 Esercizi di riepilogo

• 12.1 Esercizi
• 12.2 Soluzioni
• Distribuzioni di probabilità
• Bibliografia

• Optimal transportation.- The Wasserstein space.- Frechet means in the Wasserstein space.- Phase variation and Frechet means.- Construction of Frechet means and multicouplings.
• (source: Nielsen Book Data)

This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspects, it also provides an accessible introduction to the fundamentals of this current topic, as well as an overview that will serve as an invitation and catalyst for further research. Statistics in Wasserstein spaces represents an emerging topic in mathematical statistics, situated at the interface between functional data analysis (where the data are functions, thus lying in infinite dimensional Hilbert space) and non-Euclidean statistics (where the data satisfy nonlinear constraints, thus lying on non-Euclidean manifolds). The Wasserstein space provides the natural mathematical formalism to describe data collections that are best modeled as random measures on Euclidean space (e.g. images and point processes). Such random measures carry the infinite dimensional traits of functional data, but are intrinsically nonlinear due to positivity and integrability restrictions. Indeed, their dominating statistical variation arises through random deformations of an underlying template, a theme that is pursued in depth in this monograph.
(source: Nielsen Book Data)

• Introduction.- From lognormal to power.- The Poisson law.- Framework.- Threshold analysis.- Hazard rates.- Order statistics.- Exponent estimation.- Socioeconomic analysis.- Fractality.- Sums.- Dynamics.- Limit laws.- First digits.- Back to lognormal.- Conclusion.- Appendix.
• (source: Nielsen Book Data)

This monograph is a comprehensive and cohesive exposition of power-law statistics. Following a bottom-up construction from a foundational bedrock - the power Poisson process - this monograph presents a unified study of an assortment of power-law statistics including: Pareto laws, Zipf laws, Weibull and Frechet laws, power Lorenz curves, Levy laws, power Newcomb-Benford laws, sub-diffusion and super-diffusion, and 1/f and flicker noises. The bedrock power Poisson process, as well as the assortment of power-law statistics, are investigated via diverse perspectives: structural, stochastic, fractal, dynamical, and socioeconomic. This monograph is poised to serve researchers and practitioners - from various fields of science and engineering - that are engaged in analyses of power-law statistics.
(source: Nielsen Book Data)

• - Introduction. - Basic Probability. - Random Variables and Probability Distributions. - General Properties of Random Variables. - Joint Probability Distributions for Two Random Variables. - Probability Distribution of a Function of a Single Random Variable. - Sampling Distributions. - Asymptotic (Large-Sample) Properties of Statistics.
• (source: Nielsen Book Data)

This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.
(source: Nielsen Book Data)

• Chapter 1: Fundamentals.-
• Chapter 2: Sample Statistics are NOT Parameters.-
• Chapter 3: Confidence.-
• Chapter 4: Multiplicity and Multiple Comparisons.-
• Chapter 5: Power and the Myth of Sample Size Determination.-
• Chapter 6: Regression and Model Fitting with Collinearity.-
• Chapter 7: Overparameterization.-
• Chapter 8: Ignoring Error Control Factors and Experimental Design.-
• Chapter 9: Generalized Linear Models.-
• Chapter 10: Mixed Models and Variance Components.-
• Chapter 11: Models, Models Everywhere...Model Selection.-
• Chapter 12: Bayesian Analyses.-
• Chapter 13: The Acceptance Sampling Game.-
• Chapter 14: Nonparametric Statistics - A Strange Name.-
• Chapter 15: Autocorrelated Data and Dynamic Systems.-
• Chapter 16: Multivariate Analysis and Classification.-
• Chapter 17: Time-to-Event: Survival and Life Testing.- Index.
• (source: Nielsen Book Data)

Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
(source: Nielsen Book Data)

• Entropy and basic statistics.- Analysis of the association in two-way contingency tables.- Analysis of the association in multiway contingency tables.- Analysis of continuous variables.
• (source: Nielsen Book Data)

This book reconsiders statistical methods from the point of view of entropy, and introduces entropy-based approaches for data analysis. Further, it interprets basic statistical methods, such as the chi-square statistic, t-statistic, F-statistic and the maximum likelihood estimation in the context of entropy. In terms of categorical data analysis, the book discusses the entropy correlation coefficient (ECC) and the entropy coefficient of determination (ECD) for measuring association and/or predictive powers in association models, and generalized linear models (GLMs). Through association and GLM frameworks, it also describes ECC and ECD in correlation and regression analyses for continuous random variables. In multivariate statistical analysis, canonical correlation analysis, T2-statistic, and discriminant analysis are discussed in terms of entropy. Moreover, the book explores the efficiency of test procedures in statistical tests of hypotheses using entropy. Lastly, it presents an entropy-based path analysis for structural GLMs, which is applied in factor analysis and latent structure models. Entropy is an important concept for dealing with the uncertainty of systems of random variables and can be applied in statistical methodologies. This book motivates readers, especially young researchers, to address the challenge of new approaches to statistical data analysis and behavior-metric studies.
(source: Nielsen Book Data)

• Chapter 1: Introduction to double-truncation.-
• Chapter 2: Parametric inference under special exponential family.-
• Chapter 3: Parametric inference under location-scale family.-
• Chapter 4: Bayes inference.-
• Chapter 5: Nonparametric inference.-
• Chapter 6: Linear regression.- Appendix A: Data (if German company data are available).- Appendix B: R codes for inference under exponential family.- Appendix C: R codes for inference under location-scale family.- Appendix D: R codes for Bayes inference.- Appendix E: R codes for linear regression.
• (source: Nielsen Book Data)

This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.
(source: Nielsen Book Data)

• Chapter I: Reproducing Kernel Hilbert Space
• Chapter II: Cubature Formula
• Chapter III: Optimal Euclidean Design
• Chapter IV: Constructions of Optimal Euclidean Design
• Chapter V: Euclidean Design Theory.

This book is the modern first treatment of experimental designs, providing a comprehensive introduction to the interrelationship between the theory of optimal designs and the theory of cubature formulas in numerical analysis. It also offers original new ideas for constructing optimal designs. The book opens with some basics on reproducing kernels, and builds up to more advanced topics, including bounds for the number of cubature formula points, equivalence theorems for statistical optimalities, and the Sobolev Theorem for the cubature formula. It concludes with a functional analytic generalization of the above classical results. Although it is intended for readers who are interested in recent advances in the construction theory of optimal experimental designs, the book is also useful for researchers seeking rich interactions between optimal experimental designs and various mathematical subjects such as spherical designs in combinatorics and cubature formulas in numerical analysis, both closely related to embeddings of classical finite-dimensional Banach spaces in functional analysis and Hilbert identities in elementary number theory. Moreover, it provides a novel communication platform for "design theorists" in a wide variety of research fields.

• 1. Probability and Random Variables.-
• 2. Classical Theory of Estimation.-
• 3. Testing Hypotheses in the Presence of Nuisance Parameters.-
• 4. Testing Hypotheses in the Presence of Nuisance Parameters.-
• 5. Basic Ideas of Bayesian Methods.-
• 6. Bayesian Inference.-
• 7. Asymptotic Tools and Projections.-
• 8. Asymptotic Theory for Maximum Likelihood Estimation.-
• 9. Estimating Equations.-
• 10. Convolution Theorem and Asymptotic Efficiency.-
• 11. Asymptotic Hypothesis Test.- References.- Index.
• (source: Nielsen Book Data)

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.
(source: Nielsen Book Data)

• Laws of Large Numbers.- Random Observation Schemes.- Bootstrapping Asymptotic Laws.- Testing for (Common) Jumps.
• (source: Nielsen Book Data)

Ole Martin extends well-established techniques for the analysis of high-frequency data based on regular observations to the more general setting of asynchronous and irregular observations. Such methods are much needed in practice as real data usually comes in irregular form. In the theoretical part he develops laws of large numbers and central limit theorems as well as a new bootstrap procedure to assess asymptotic laws. The author then applies the theoretical results to estimate the quadratic covariation and to construct tests for the presence of common jumps. The simulation results show that in finite samples his methods despite the much more complex setting perform comparably well as methods based on regular data. About the Author: Dr. Ole Martin completed his PhD at the Kiel University (CAU), Germany. His research focuses on high-frequency statistics for semimartingales with the aim to develop methods based on irregularly observed data.
(source: Nielsen Book Data)

• Introduction.- Reliability and Validity.- Interrater Agreement.- Indices.- References.- Index.- Notation.
• (source: Nielsen Book Data)

This introductory book enables researchers and students of all backgrounds to compute interrater agreements for nominal data. It presents an overview of available indices, requirements, and steps to be taken in a research project with regard to reliability, preceded by agreement. The book explains the importance of computing the interrater agreement and how to calculate the corresponding indices. Furthermore, it discusses current views on chance expected agreement and problems related to different research situations, so as to help the reader consider what must be taken into account in order to achieve a proper use of the indices. The book offers a practical guide for researchers, Ph.D. and master students, including those without any previous training in statistics (such as in sociology, psychology or medicine), as well as policymakers who have to make decisions based on research outcomes in which these types of indices are used.
(source: Nielsen Book Data)

• 1. Imbalance Data.-
• 2. Weighted Logistic Regression.-
• 3. Beta-Maxent.-
• 4. Generalized-t Statistic.-
• 5. Machine Learning Methods for Imbalance Data.
• (source: Nielsen Book Data)

This book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets.
(source: Nielsen Book Data)

• Part I. Statistical Process Control.-
• Chapter 1. Some Recent Studies in Statistical Process Control.-
• Chapter 2. Statistical Quality Control And Reliability Analysis Using the Birnbaum-Saunders Distribution with Industrial Applications.-
• Chapter 3. Statistical System Monitoring (SSM) for Enterprise-Level Quality Control.-
• Chapter 4. Enhanced Cumulative Sum Charts based on Ranked Set Sampling.-
• Chapter 5. A Survey of Control Charts for Simple Linear Profile Processes with Authcorrelation.-
• Chapter 6. Sequential Monitoring of Circular processes related to the von Mises Distribution.- Part II. Acceptance Sampling Plans.-
• Chapter 7. Time Truncated Life Test Using the Generalized Multiple Dependent State Sampling plans for Various Life Distributions.-
• Chapter 8. Decision Theoretic Sampling Plan for One-parameter Exponential Distribution under Type-I and Type-I Hybrid Censoring Schemes.-
• Chapter 9. Economical Sampling Plans with Warranty.-
• Chapter 10. Design of Reliability Acceptance Sampling Plans under Partially Accelerated Life Test.- Part III. Reliability Testing and Designs.
• Chapter 11. Bayesian Sequential Design Based on Dual Objectives for Accelerated Life Tests.-
• Chapter 12. The Stress-strength Models for the Proportional Hazards Family and Proportional Reverse Hazards Family.-
• Chapter 13. A Degradation Based on the Wiener Process Assuming non-normal Distributed Measurement Errors.-
• Chapter 14. An Introduction of Generalized Linear Model Approach to Accelerated Life Test Planning with Type-I Censoring.-
• Chapter 15. Robust Design in the Case of Data Contamination and Model Departure.-
• Chapter 16. Defects Driven Yield and Reliability Modeling for Semiconductor Manufacturing.
• (source: Nielsen Book Data)

This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today's fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.
(source: Nielsen Book Data)

• Part I
• Basic Probability Theory
• Experimental Design and the Basics of Statistics: Signal detection Theory (SDT)
• The Core Concept of Statistics
• Variations on the t-test
• PART II
• The Multiple Testing Problem
• ANOVA
• Experimental design: Model Fits, Power, and Complex Designs
• Correlation
• PART III
• Meta-analysis
• Understanding replication
• Magnitude of excess success
• Suggested improvements and challenges.

This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.

• Rank Properties for Centred Three-way Arrays - C. Albers (Univ. of Groningen) et al.- Principal Component Analysis of Complex Data and Application to Climatology - S. Camiz (La Sapienza Univ. of Rome) et al.- Clustering upper level units in multilevel models for ordinal data - L. Grilli (Univ. of Florence) et al.- A Multilevel Heckman Model To Investigate Financial Assets Among Old People In Europe - O. Paccagnella (univ. of Padua) et al.- Multivariate stochastic downscaling with semicontinuous data - L. Paci (univ. of Bologna) et al.- Motivations and expectations of students' mobility abroad: a mapping technique - V. Caviezel (Univ. of Bergamo) et al.- Comparing multi-step ahead forecasting functions for time series clustering - M. Corduas (Univ. of Naples Federico II) et al.- Electre Tri-Machine Learning Approach to the Record Linkage - V. Minnetti (La Sapienza Univ. of Rome) et al.- . MCA Based Community Detection - C. Drago (Univ. of Rome Niccolo Cusano).- Classi fying social roles by network structures - S. Gozzo (univ. of Catania) et al.- Bayesian Networks For Financial Markets Signals Detection - A. Greppi (univ.of Pavia) et al.- Finite sample behaviour of MLE in network autocorrelation models - M. La Rocca (Univ. of Salerno) et al.- Classification Models as Tools of Bankruptcy Prediction - Polish Experience - J. Pochiecha (Cracow university) et al.- Clustering macroseismic fields by statistical data depth functions - C. Agostinelli (Univ. of Trento).- Depth based tests for circular antipodal symmetry - G. Pandolfo (Univ. of Cassino) et al.- Estimating The Effect Of Prenatal Care On Birth Outcomes - E. Sironi (Sacro Cuore University) et al.- Bifurcations And Sunspots In Continuous Time Optimal Models With Externalities - B.Venturi (Univ. of Cagliari) et al.- Enhancing Big Data Exploration with Faceted Browsing - S. Bergamaschi (Univ. of Modena and Reggio Emilia) et al.- Big data meet pharmaceutical industry: an application on social media data - C. Liberati (Univ. of Milan Bicocca) et al.- From Big Data to information: statistical issues through a case study - S. Signorelli (Univ. of Bergamo) et al.- Quality of Classification approaches for the quantitative analysis of international conflict - A.F.X. Wilhelm (Jacobs Univ. Bremen).- P-splines based clustering as a general framework: some applications using different clustering algorithms - C. Iorio (Univ. of Naples Federico II) et al.- A graphical copula-based tool for detecting tail dependence - R. Pappada (univ. of Trieste) et al.- Comparing spatial and spatio-temporal FPCA to impute large continuous gaps in space - M. Ruggeri (Univ. of Palermo) et al.- Exploring Italian students' performances in the SNV test: a quantile regression perspective - A. Costanzo (National Institute for the Evaluation of Education and Training - INVALSI) et al.
• (source: Nielsen Book Data)

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8-10, 2015.
(source: Nielsen Book Data)