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Distribution (Probability theory) --- Mathematical statistics --- Distribution (Théorie des probabilités) --- Statistique mathématique

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The last book dedicated to this topic was published in 1978, and since then there have been many developments in survival analysis, Competing Risks, and in statistical methods in general. The subject is now drawing increasing interest from engineers and biologists. Written by an acknowledged expert, this book thoroughly examines the probability framework and statistical analysis of data of Competing Risks. With a dearth of modern treatments of the subject and the importance of its methods, Classical Competing Risks fills a long-standing gap in the literature with a carefully organized exposition filled with real data sets, numerous examples, and clear, readable prose.

Mathematical statistics --- Competing risks --- Failure time data analysis --- Temps entre défaillances, Analyse des --- 519.2 --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Survival analysis (Biometry) --- Risks, Competing --- Distribution (Probability theory) --- Estimation theory --- Random variables --- Competing risks. --- Failure time data analysis. --- Temps entre défaillances, Analyse des

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This book is a practical guide to help researchers draw valid causal inferences from small-scale clinical intervention studies. It should be of interest to teachers of, and students in, courses with an experimental clinical component, as well as clinical researchers. Inferential statistics used in the analysis of group data are frequently invalid for use with data from single-case experimental designs. Even non-parametric rank tests provide, at best, approximate solutions for only some single-case (and small-'n' ) designs. Randomization (Exact) tests, on the other hand, can provide valid statistical analyses for all designs that incorporate a random procedure for assigning treatments to subjects or observation periods, including single-case designs. These Randomization tests require large numbers of data rearrangements and have been seldom used, partly because desktop computers have only recently become powerful enough to complete the analyses in a reasonable time. Now that the necessary computational power is available, they continue to be under-used because they receive scant attention in standard statistical texts for behavioral researchers and because available programs for running the analyses are relatively inaccessible to researchers with limited statistical or computing interest. This book is first and foremost a practical guide, although it also presents the theoretical basis for Randomization tests. Its most important aim is to make these tests accessible to researchers for a wide range of designs. It does this by providing programs on CD-ROM that allow users to run analyses of their data within a standard package (Minitab, Excel, or SPSS) with which they are already familiar. No statistical or computing expertise is required to use these programs. This is the "new stats" for single-case and small-'n' intervention studies, and anyone interested in this research approach will benefit.

Experimental design. --- Statistical hypothesis testing. --- Experimental design --- Statistical hypothesis testing --- Hypothesis testing (Statistics) --- Significance testing (Statistics) --- Statistical significance testing --- Testing statistical hypotheses --- Distribution (Probability theory) --- Hypothesis --- Mathematical statistics --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Experiments --- Methodology --- Statistiek (theorie) --- Tests (geneeskunde) --- Test (geneeskunde)

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These notes represent our summary of much of the recent research that has been done in recent years on approximations and bounds that have been developed for compound distributions and related quantities which are of interest in insurance and other areas of application in applied probability. The basic technique employed in the derivation of many bounds is induc­ tive, an approach that is motivated by arguments used by Sparre-Andersen (1957) in connection with a renewal risk model in insurance. This technique is both simple and powerful, and yields quite general results. The bounds themselves are motivated by the classical Lundberg exponential bounds which apply to ruin probabilities, and the connection to compound dis­ tributions is through the interpretation of the ruin probability as the tail probability of a compound geometric distribution. The initial exponential bounds were given in Willmot and Lin (1994), followed by the nonexpo­ nential generalization in Willmot (1994). Other related work on approximations for compound distributions and applications to various problems in insurance in particular and applied probability in general is also discussed in subsequent chapters. The results obtained or the arguments employed in these situations are similar to those for the compound distributions, and thus we felt it useful to include them in the notes. In many cases we have included exact results, since these are useful in conjunction with the bounds and approximations developed.

Insurance --- Distribution (Probability theory) --- Statistical methods --- Stochastic processes --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Distribution (Théorie des probabilités) --- Statistical methods. --- Actuarial statistics --- Insurance statistics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Mathematics --- Probabilities. --- Statistics . --- Economics, Mathematical . --- Probability Theory and Stochastic Processes. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Quantitative Finance. --- Economics --- Mathematical economics --- Econometrics --- Statistical analysis --- Statistical data --- Statistical science --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Methodology --- Insurance - Statistical methods

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This volume contains the papers selected for presentation at IPCO VIII, the Eighth Conference on Integer Programming and Combinatorial Optimization, Utrecht, The Netherlands, 2001. This meeting isa forum for researchers and practitioners working on various aspects of integer programming and combi- torial optimization. The aim is to present recent developments in theory, com- tation, and application of integer programming and combinatorial optimization. Topics include, but are not limited to: approximation algorithms, branch and bound algorithms, computational biology, computational complexity, compu- tional geometry, cutting plane algorithms, diophantine equations, geometry of numbers, graph and network algorithms, integer programming, matroids and submodular functions, on-line algorithms, polyhedral combinatorics, scheduling theory and algorithms, and semide nit e programs. IPCO was established in 1988 when the rs t IPCO program committee was formed. The locations and years of the seven rs t IPCO conferences were: IPCO I, Waterloo (Canada) 1990, IPCO II, Pittsburgh (USA) 1992, IPCO III, - ice (Italy) 1993, IPCO IV, Copenhagen (Denmark) 1995, IPCO V, Vancouver (Canada) 1996, IPCO VI, Houston (USA) 1998, IPCO VII, Graz (Austria) 1999. IPCO is held every year in which no MPS (Mathematical Programming Society) International Symposium takes place. Since the MPS meeting is triennial, IPCO conferences are held twice in every three-year period. Asa rule, IPCO is held somewhere in Northern America in even years, and somewhere in Europe in odd years.

Integer programming --- Combinatorial optimization --- Operations Research --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Congresses --- Mathematics. --- Information technology. --- Business --- Algorithms. --- Computer science --- Probabilities. --- Combinatorics. --- Probability Theory and Stochastic Processes. --- Algorithm Analysis and Problem Complexity. --- Discrete Mathematics in Computer Science. --- IT in Business. --- Data processing. --- Programming (Mathematics) --- Distribution (Probability theory. --- Computer software. --- Computational complexity. --- Combinatorics --- Algebra --- Mathematical analysis --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Complexity, Computational --- Electronic data processing --- Machine theory --- Software, Computer --- Computer systems --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Computer science—Mathematics. --- Business—Data processing. --- Algorism --- Arithmetic --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Foundations --- Integer programming - Congresses --- Combinatorial optimization - Congresses