الصفحة 17
الصفحة 17
Advanced Multivariate Statistics with Matrices
Presents important tools and techniques for treating problems in m- ern multivariate statistics in a systematic way. The ambition is to indicate new directions as well as to present the classical part of multivariate statistical analysis in this framework.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Adaptive Information Systems and Modelling in Economics and Management Science
Learning and adaption are key features of "real economies". Studying interesting real phenomena like innovation, industry evolution or the role of expectation formulation in financial markets thus necessitates novel methods of data analysis and modelling. This title covers statistical models of heterogeneity, artificial consumer markets, models of adaptive expectation formulation in financial markets and agent-based models of industry evolution, product diversification and energy markets. The joint findings are presented in a manner that is interesting both for readers with a background in economics/management and mathematics and statistics and also for non-expert readers because it allows them to grasp the ideas of modern management science. This book thus provides a unique integrated toolbox for building realistic agent-based models of learning and adaption in a variety of settings based on sound data analysis.
Accounting and Statistical Analyses for Sustainable Development : Multiple Perspectives and Information-Theoretic Complexity Reduction
In this book Claudia Lemke develops a comprehensive Multi-Level Sustainable Development Index (MLSDI) that is applicable to micro, meso, and macro objects by conducting methodological and empirical research.
A proposed model for predicting financial Loss of private conventional and Islamic banks in Syria
This study aimed to find a model consisting of a set of financial ratios in which each ratio has its own weight that indicate its importance to predict probability of financial loss of conventional and Islamic banks in Syria. The early prediction warns the concerned parties that they can intervene and take corrective actions before the collapses of bank. To achieve this ratios of conventional and Islamic Syrian banks were analyzed using Binary logistic regression from the period of 2011-2020 The statistical results show that the logistic regression model is accurate to predict the probability of a financial loss in conventional banks about 82.2%, 81.3%, 80.1%, 78% before 90 days ,180 days, 270 days, one year respectively. We can generally use five variables (Non-performing debt, return on equity, size, growth rate and financing portfolio ratio) in bank's financial loss prediction, but for Islamic banks, no significant values were shown so we can’t find logistic regression model is accurate for Islamic banks.
A History of Parametric Statistical Inference from Bernoulli to Fischer, 1713-1935
This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.
A First Course in Statistics for Signal Analysis
This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation.
A First Course in Statistical Inference
Offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data. Aimed at beginning undergraduate students in mathematics, it presents the concepts underpinning frequentist statistical theory. Written in a conversational and informal style, this concise text concentrates on ideas and concepts, with key theorems stated and proved. Detailed worked examples are included and each chapter ends with a set of exercises, with full solutions given at the back of the book. Examples using R are provided throughout the book, with a brief guide to the software included. Topics covered in the book include: sampling distributions, properties of estimators, confidence intervals, hypothesis testing, ANOVA, and fitting a straight line to paired data.
A Course in Enumeration
Leads the reader in a leisurely way from the basic notions to a variety of topics, ranging from algebra to statistical physics. Its aim is to introduce the student to a fascinating field, and to be a source of information for the professional mathematician who wants to learn more about the subject.
A concise guide to market research : The process, data, and methods using IBM SPSS Statistics, 3rd
Offers an easily accessible and comprehensive guide to the entire market research process, from asking market research questions to collecting and analyzing data by means of quantitative methods. It is intended for all readers who wish to know more about the market research process, data management, and the most commonly used methods in market research. The book helps readers perform analyses, interpret the results, and make sound statistical decisions using IBM SPSS Statistics. Hypothesis tests, ANOVA, regression analysis, principal component analysis, factor analysis, and cluster analysis, as well as essential descriptive statistics, are covered in detail.
A Benchmark Approach to Quantitative Finance
The general framework is used to provide an understanding of the nature of stochastic volatility. The book is intended for a wide audience that includes quantitative analysts, postgraduate students and practitioners in finance, economics and insurance. It aims to be a self-contained, accessible but mathematically rigorous introduction to quantitative finance for readers that have a reasonable mathematical or quantitative background. Finally, the book should stimulate interest in the benchmark approach by describing some of its power and wide applicability.
