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The Effect An Introduction to Research Design and Causality

Stochastic Processes with R An Introduction

Digital Image Processing An Algorithmic Approach with MATLAB

Interactive Web-Based Data Visualization with R plotly and shiny

Interactive Web-Based Data Visualization with R plotly and shiny

The richly illustrated Interactive Web-Based Data Visualization with R plotly and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools you will impress your colleagues with your ability to quickly generate more informative engaging and reproducible interactive graphics using free and open source software that you can share over email export to pdf and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link animate and arrange multiple plots in standalone HTML from R Embed modify and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous discrete and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering but you will also learn about other R packages that support different phases of a data science workflow such as tidyr dplyr and tidyverse. Along the way you will gain insight into best practices for visualization of high-dimensional data statistical graphics and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.

GBP 66.99
1

A Factor Model Approach to Derivative Pricing

A Factor Model Approach to Derivative Pricing

Written in a highly accessible style A Factor Model Approach to Derivative Pricing lays a clear and structured foundation for the pricing of derivative securities based upon simple factor model related absence of arbitrage ideas. This unique and unifying approach provides for a broad treatment of topics and models including equity interest-rate and credit derivatives as well as hedging and tree-based computational methods but without reliance on the heavy prerequisites that often accompany such topics. Key features A single fundamental absence of arbitrage relationship based on factor models is used to motivate all the results in the book A structured three-step procedure is used to guide the derivation of absence of arbitrage equations and illuminate core underlying concepts Brownian motion and Poisson process driven models are treated together allowing for a broad and cohesive presentation of topics The final chapter provides a new approach to risk neutral pricing that introduces the topic as a seamless and natural extension of the factor model approach Whether being used as text for an intermediate level course in derivatives or by researchers and practitioners who are seeking a better understanding of the fundamental ideas that underlie derivative pricing readers will appreciate the book‘s ability to unify many disparate topics and models under a single conceptual theme. James A Primbs is an Associate Professor of Finance at the Mihaylo College of Business and Economics at California State University Fullerton.

GBP 175.00
1

Mixture Model-Based Classification

Mixture Model-Based Classification

This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative modern reference in the mixture modeling literature. (Douglas Steinley University of Missouri)Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered from mixtures with components that parameterize skewness and/or concentration right up to mixtures of multiple scaled distributions. Several other important topics are considered including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a clusterPaul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification with particular attention to clustering applications and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.

GBP 44.99
1

Multilevel Modeling Using R

Multilevel Modeling Using R

Like its bestselling predecessor Multilevel Modeling Using R Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models including rank based heavy tailed distributions and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.

GBP 56.99
1

Statistical Theory A Concise Introduction

Statistical Theory A Concise Introduction

Designed for a one-semester advanced undergraduate or graduate statistical theory course Statistical Theory: A Concise Introduction Second Edition clearly explains the underlying ideas mathematics and principles of major statistical concepts including parameter estimation confidence intervals hypothesis testing asymptotic analysis Bayesian inference linear models nonparametric statistics and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions theorems and proofs. Based on the authors’ lecture notes the book is self-contained which maintains a proper balance between the clarity and rigor of exposition. In a few cases the authors present a sketched version of a proof explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments. Features: Second edition has been updated with a new chapter on Nonparametric Estimation; a significant update to the chapter on Statistical Decision Theory; and other updates throughout No requirement for heavy calculus and simple questions throughout the text help students check their understanding of the material Each chapter also includes a set of exercises that range in level of difficulty Self-contained and can be used by the students to understand the theory Chapters and sections marked by asterisks contain more advanced topics and may be omitted Special chapters on linear models and nonparametric statistics show how the main theoretical concepts can be applied to well-known and frequently used statistical tools The primary audience for the book is students who want to understand the theoretical basis of mathematical statistics—either advanced undergraduate or graduate students. It will also be an excellent reference for researchers from statistics and other quantitative disciplines. | Statistical Theory A Concise Introduction

GBP 74.99
1

Random Circulant Matrices

Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the properties of the eigenvalues for various types of circulant matrices such as the usual circulant the reverse circulant and the k-circulant when the dimension of the matrices grow and the entries are random. In particular the behavior of the spectral distribution of the spectral radius and of the appropriate point processes are developed systematically using the method of moments and the various powerful normal approximation results. This behavior varies according as the entries are independent are from a linear process and are light- or heavy-tailed. Arup Bose obtained his B. Stat. M. Stat. and Ph. D. degrees from the Indian Statistical Institute. He has been on its faculty at the Theoretical Statistics and Mathematics Unit Kolkata India since 1991. He is a Fellow of the Institute of Mathematical Statistics and of all three national science academies of India. He is a recipient of the S. S. Bhatnagar Prize and the C. R. Rao Award. He is the author of three books: Patterned Random Matrices Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee) and U-Statistics M_m-Estimators and Resampling (with Snigdhansu Chatterjee). Koushik Saha obtained a B. Sc. in Mathematics from Ramakrishna Mission Vidyamandiara Belur and an M. Sc. in Mathematics from Indian Institute of Technology Bombay. He obtained his Ph. D. degree from the Indian Statistical Institute under the supervision of Arup Bose. His thesis on circulant matrices received high praise from the reviewers. He has been on the faculty of the Department of Mathematics Indian Institute of Technology Bombay since 2014. | Random Circulant Matrices

GBP 44.99
1

An Introduction to Optimization with Applications in Machine Learning and Data Analytics

An Introduction to Optimization with Applications in Machine Learning and Data Analytics

The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering computer science economics statistics and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters are used in self-driving cars play a great role in machine learning and can be used in such places as determining a batting order in a Major League Baseball game. Additionally optimization has seemingly limitless other applications in business and industry. In short knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used as this is common in business but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica and the computer science students to write their own programs in Java or Python. | An Introduction to Optimization with Applications in Machine Learning and Data Analytics

GBP 82.99
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