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Analysis of Longitudinal Data
This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Highly Structured Stochastic Systems
Through this text, the author aims to make recent developments in the title subject (a modern strategy for the creation of statistical models to solve 'real world' problems) accessible to graduate students and researchers in the field of statistics.
An Introduction to Model-Based Survey Sampling with Applications
This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions.
Time Series: A Biostatistical Introduction
Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computational tools. Methodology that was originally developed for specialized applications, for example in finance or geophysics, is now widely available within general statistical packages. The second edition of Time Series: A Biostatistical Introduction is an introductory account of time series analysis, written from the perspective of applied statisticians whose interests lie primarily in the biomedical and health sciences. This edition has a stronger focus on substantive applications, in which each statistical analysis is directed at a specific researc...
Principles of Multivariate Analysis
Multivariate analysis is necessary whenever more than one characteristic is observed on each individual under study. Applications arise in very many areas of study. This book provides a comprehensive introduction to available techniques for analysing date of this form, written in a style that should appeal to non-specialists as well as to statisticians. In particular, geometric intuition is emphasized in preference to algebraic manipulation wherever possible. The new edition includes a survey of the most recent developments in the subject.
Time Series Analysis by State Space Methods
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Advances in the Sociology of Trust and Cooperation
The problem of cooperation is one of the core issues in sociology and social science more in general. The key question is how humans, groups, organizations, institutions, and countries can avoid or overcome the collective good dilemmas that could lead to a Hobbesian "war of all against all". The chapters in this book provide state of the art examples of research on this crucial topic. These include theoretical, laboratory, and field studies on trust and cooperation, thereby approaching the issue in three complementary and synergetic ways. The theoretical work covers articles on trust and control, reputation formation, and paradigmatic articles on the benefits and caveats of abstracting reali...
Kif
Routes, bribery, fight against crime, mafia-style organizations, connections with hard drugs, money laundering and even more disquieting world scenarios A trip among the illicit traffics and the main characters in an ongoing foul play. You will unlikely find such impressive information about hashish, drug-trafficking, economic backstage and interviews with traffickers and police officers, all rallied in only one book so clearly expressed and direct.
Big Data Analytics in Oncology with R
Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area. Features: Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
