الصفحة 1
الصفحة 1
New Vision of Metformin in treating cancer
The anti-diabetic drug metformin is rapidly emerging as a potential anticancer agent. Metformin is a biguanide that is effective in treating type 2 diabetes and the insulin resistance syndromes, improves insulin resistance by reducing hepatic gluconeogensis and by enhancing glucose uptake by skeletal muscle. Metformin can reduce the incidence of cancers and can reduce the mortality from cancers, increase the response to treatment cancer cells when using radiotherapy and chemotherapy, reduce the likelihood of relapse. Diabetes can be a factor in the occurrence of various types of cancer, and develop a variety of cancers such as colo-rectal, pancreas and liver cancers, compared to non-diabetic patients. Incidence of various cancers is high among patients of T2DM due to insulin resistance and mitogenic effects caused by hyperglycemia.
New Introduction to Multiple Time Series Analysis
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
Multivariate and Mixture Distribution Rasch Models : Extensions and Applications
This volume covers extensions of the Rasch model, one of the most researched and applied models in educational research and social science. This collection contains 22 chapters by some of the most recognized international experts in the field. They cover topics ranging from general model extensions to applications in fields as diverse as cognition, personality, organizational and sports psychology, and health sciences and education.
Models for Discrete Longitudinal Data
This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. The book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow.
Model Based Inference in the Life Sciences : A Primer on Evidence
The abstract concept of "information" can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this set—a formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori set—multimodel inference.
Information criteria and statistical modeling
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Head and neck cancer recurrence : Evidence-based, multidisciplinary management
The first comprehensive, evidence-based reference on this complex condition, providing state-of-the-art strategies for treating and managing recurrence at each site within the head and neck. With this book at hand, specialists will have guidelines for detecting the presence of a tumor, evaluating the extent and likelihood of successful salvage, determining the most effective treatment modalities, and assessing the potential impact on outcome and function.
Geostatistics Banff 2004
The five major sections are: theory, mining, petroleum, environmental and other applications. The first section showcases new and innovative ideas in the theoretical development of geostatistics as a whole; these ideas will have large impact on (1) the directions of future geostatistical research, and (2) the conventional approaches to heterogeneity modelling in a wide range of natural resource industries. The next four sections are focused on applications and innovations relating to the use of geostatistics in specific industries. Historically, mining, petroleum and environmental industries have embraced the use of geostatistics for uncertainty characterization, so these three industries are identified as major application areas. The last section is open for innovative geostatistical application to address the issues and impact of uncertainty in other industries.
Génetique statistique = Statistical genetics
Presents the main statistical tools useful in genetics: significance tests, analysis methods based on the likelihood function, EM algorithm, modeling, analysis of variance, hierarchical classifications, multiple comparisons, etc. All of them shed light on a number of biological phenomena such as carcinogenesis, population genetics, Hardy-Weinberg equilibrium, natural selection, mutations, heredity, coalescence processes, and even evolution. This book is intended for mathematicians and biologists alike. Written with a great concern for clarity, it is also accessible to non-specialists who will be able, thanks to it, to strengthen their theoretical base and above all to develop their know-how through very concrete applications.
Fundamentals of clinical research : Bridging medicine, statistics and operations
The scope of clinical research is to evaluate the effect of a treatment on the evolution of a disease in the human species.The treatment can be pharmacological, surgical, psychological/behavioral or organizational/logistic. The disease, intended as an impairment of a state of well-being or a condition capable of provoking such impairment over time, can be universally accepted as such (e.g. a cancer or a bone fracture) or perceived as such only by limited groups of individuals in a given cultural context (e.g. hair loss or weight gain). The course of the disease that ones wishes to change can be the one with no intervention or, more frequently, the one observed with the available treatment. The evaluation of the effect of a treatment on the course of a disease is a lengthy process, which progresses in increasingly complex stages.
Forecasting with Exponential Smoothing : The State Space Approach
Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.
Estimation in Conditionally Heteroscedastic Time Series Models
ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
Discrete Multivariate Analys : Theory and Practice
Thes book is a most welcome contribution to an interesting and lively subject." -- NatureOriginally published in 1974, this book is a reprint of a classic, still-valuable text.
Dependence in Probability and Statistics
This book gives a detailed account of some recent developments in the field of probability and statistics for dependent data. The book covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. A special section is devoted to statistical estimation problems and specific applications. The book is written as a succession of papers by some specialists of the field, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field. The first part of the book considers some recent developments on weak dependent time series, including some new results for Markov chains as well as some developments on new notions of weak dependence. This part also intends to fill a gap between the probability and statistical literature and the dynamical system literature. The second part presents some new results on strong dependence with a special emphasis on non-linear processes and random fields currently encountered in applications. Finally, in the last part, some general estimation problems are investigated, ranging from rate of convergence of maximum likelihood estimators to efficient estimation in parametric or non-parametric time series models, with an emphasis on applications with non-stationary data.
Correlated Data Analysis : Modeling, Analytics, and Applications
Presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models.
Linear Models and Generalizations : Least Squares and Alternatives
Gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Legitimacy Needs as Drivers of Business Exit
A diversified firm’s withdrawal from a business unit, i.e. business exit, is a significant phenomenon in management practice. Although divestitures are highly relevant in practice, the acquisition of business units attracts much more attention in strategic management research. Carolin Decker develops and empirically applies a framework in which business exits serve the purpose of re-establishing a firm’s previously harmed legitimacy. She suggests four types of legitimacy needs that are to be satisfied with the divestiture of a business unit and the simultaneous pursuit of strategic reorientation. The author tests the theoretical framework with secondary data on 213 business exits. Her findings support the idea that legitimacy needs drive the likelihood of fit-enhancing business exits in divesting firms.
Landslide Risk Assessment ; 2nd ed.
Provides guidance to practitioners on establishing the likelihood and extent to which future slope failures could adversely impact society and affect people and property. The only book to focus on risk and landslides, using examples from across the globe, Landslide Risk Assessment examines a variety of approaches to landslide risk assessment and management, introducing the key challenges that practitioners will need to overcome: estimating the probability and consequences of landsliding, combining these to develop a measure of the risk, and making the transition between risk assessment and risk management.
Capital project management ; Vol.2 : Capital project finance
Describes the strategic challenge of adding real economic value, properly and rigorously defined. The author explains how this is accomplished through the capital budgeting process; discusses the importance of free cash flow and finally, capital projects, as financial options, are discussed, as a way to manage risk while enhancing the likelihood of project approval.
