This is an ideal textbook for researchers interested in sampling methods, survey methodologists in government organizations, academicians, and graduate students in statistics, mathematics and biostatistics. This textbook makes advanced topics easy to grasp for the readers. Many theoretical discussions about the latest estimators in literature are incorporated into almost all the chapters. This feature helps the academicians acquire necessary skills to follow up recent studies on their own. More than just an introductory text, the reader, who has mastered the contents of this book, should attain a level so as to be able to make a major contribution to the technical and research literature of sampling theory.

Time series analysis is applicable in a variety of disciplines, such as business administration, economics, public finance, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizational planning activities. Time series analysis is employed by many different organizations such as hospitals, universities, commercial enterprises or government organizations in order to forecast future scenarios. Therefore, many time series forecasting methods have been proposed and improved in statistical literature. Linear models such as Box-Jenkins methods were earlier used in many situations. Then, to overcome the restrictions of these linear models and to account for certain nonlinear patterns observed in real problems, some nonlinear models are also presented in literature. However, since these nonlinear models were developed for specific nonlinear patterns, they are not suitable for modeling other types of nonlinearity in time series. In recent years, efficient and advanced techniques such as artificial neural networks, fuzzy time series and some hybrid models have been used to forecast any kind of real life time series analyses. Both theoretical and empirical findings in academic literature show that these approaches give comparatively reliable forecasts than those obtained from conventional forecasting methods. In addition, conventional models require some assumptions such as linearity and normal distribution or cannot be utilized efficiently for some real time series such as temperature and share prices of stockholders since these kind of series contain some uncertainty. However, when advanced methods such as neural networks and fuzzy time series are used to forecast time series, there is no need to satisfy any assumption and the time series containing uncertainty can be forecasted efficiently. This e-book contains recent effective applications and descriptions of these advanced forecasting methods. Readers will learn how these methods work and how these approaches can be used to forecast real life time series. In addition, the hybrid forecasting model approach, which combines different methods to obtain better forecast results, is also explained. Readers can also find the applications of hybrid forecasting models in this e-book. This e-book also enables skilled statisticians to create a new hybrid forecasting model suitable for their own objectives. Data presented in this e-book is problem based and is taken from real life situations. This e-book is a valuable resource for students, statisticians and working professionals interested in advanced time series analysis.

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes applications of some powerful forecasting approaches with a focus on fuzzy time series methods. Chapters integrate these methods with concepts such as neural networks, high order multivariate systems, deterministic trends, distance measurement and much more. The chapters are contributed by eminent scholars and serve to motivate and accelerate future progress while introducing new branches of time series forecasting. This book is a valuable resource for MSc and PhD students, academic personnel and researchers seeking updated and critically important information on the concepts of advanced time series forecasting and its applications.

1. Since the introduction of the information measure widely known as Shannon entropy, quantifiers based on information theory and concepts such as entropic forms and statistical complexities have proven to be useful in diverse scientific research fields. This book contains introductory tutorials suitable for the general reader, together with chapters dedicated to the basic concepts of the most frequently employed information measures or quantifiers and their recent applications to different areas, including physics, biology, medicine, economics, communication and social sciences. As these quantifiers are powerful tools for the study of general time and data series independently of their sources, this book will be useful to all those doing research connected with information analysis. The tutorials in this volume are written at a broadly accessible level and readers will have the opportunity to acquire the knowledge necessary to use the information theory tools in their field of interest.

Real and complex exponential data fitting is an important activity in many different areas of science and engineering, ranging from Nuclear Magnetic Resonance Spectroscopy and Lattice Quantum Chromodynamics to Electrical and Chemical Engineering, Vision and Robotics. The most commonly used norm in the approximation by linear combinations of exponentials is the l2 norm (sum of squares of residuals), in which case one obtains a nonlinear separable least squares problem. A number of different methods have been proposed through the years to solve these types of problems and new applications appear daily. Necessary guidance is provided so that care should be taken when applying standard or simplified methods to it. The described methods take into account the separability between the linear and nonlinear parameters, which have been quite successful. The accessibility of good, publicly available software that has been very beneficial in many different fields is also considered. This Ebook covers the main solution methods (Variable Projections, Modified Prony) and also emphasizes the applications to different fields. It is considered essential reading for researchers and students in this field.

This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides studies for readers. The chapters address a comprehensive range of advanced data technologies used for statistical modeling towards predictive analytics. Topics included in this book include: - Categorized machine learning algorithms - Player monopoly in cricket teams. - Chain type estimators - Log type estimators - Bivariate survival data using shared inverse Gaussian frailty models - Weblog analysis - COVID-19 epidemiology This reference book will be of significant benefit to the predictive analytics community as a useful guide of the latest research in this emerging field.

The reference considers probability theory in two main domains: fuzzy set theory, and quantum models. Readers will learn about the Kolmogorov probability theory and its implications in these two areas. Other topics covered include intuitionistic fuzzy sets (IF-set) limit theorems, individual ergodic theorem and relevant statistical applications (examples from correlation theory and factor analysis in Atanassov intuitionistic fuzzy sets systems, the individual ergodic theorem and the Poincaré recurrence theorem). This book is a useful resource for mathematics students and researchers seeking information about fuzzy sets in quantum spaces.

Introduction: <p></p> Mathematicians and statisticians have made significant academic progress on the subject of distribution theory in the last two decades, and this area of study is becoming one of the main statistical tools for the analysis of lifetime (survival) data. In many ways, lifetime distributions are the common language of survival dialogue because the framework subsumes many statistical properties of interest, such as reliability, entropy and maximum likelihood. <p></p> Recent Advances in Lifetime and Reliability Models provides a comprehensive account of models and methods for lifetime models. Building from primary definitions such as density and hazard rate functions, this book presents readers a broad framework on distribution theory in survival analysis. This framework covers classical methods - such as the exponentiated distribution method – as well as recent models explaining lifetime distributions, such as the beta family and compounding models. Additionally, a detailed discussion of mathematical and statistical properties of each family, such as mixture representations, asymptotes, types of moments, order statistics, quantile functions, generating functions and estimation is presented in the book. <p></p> Key Features: <p></p> - presents information about classical and modern lifetime methods <p></p> - covers key properties of different models in detail <p></p> - explores regression models for the beta generalized family of distributions <p></p> - focuses information on both theoretical fundamentals and practical aspects of implementing different models <p></p> - features examples relevant to business engineering and biomedical sciences <p></p> Recent Advances in Lifetime and Reliability Models will equip students, researchers and working professionals with the information to make extensive use of observational data in a variety of fields to create inferential models that make sense of lifetime data.

SPSS is beneficial for students and researchers in psychology, sociology, psychiatry, and other behavioural sciences, as it performs a wide range of univariate and multivariate operations commonly used in these fields. Introductory Statistical Procedures with SPSS is an easy to use textbook that presents the core concepts of statistical theory with applications in SPSS. The goal of this book is to give learners simple and clear instructions on how to use recent versions of SPSS, to perform a range of statistical studies. The book chapters explain elementary statistics (descriptive and inferential) giving a detailed understanding of the discussed topics. Each chapter focuses on a specific type of analytical procedure that can be used to understand a wide range of data sets, the majority of which are from the social and behavioural sciences. Key Features: - Simple and clear instructions to use SPSS (Version 21) in 10 chapters - Introduces the reader to core descriptive statistical and probability measures - Covers inferential statistics - Covers regression and correlation analysis - Covers multivariate statistics - Presents many illustrative examples and theoretical problems at the end of each chapter with real-life data Introductory Statistical Procedures with SPSS is an essential guide for anyone who is interested in learning statistical applications of SPSS. Readers with no prior knowledge of statistics, students at all academic levels and professionals alike will benefit from the information in this textbook.