Order statistics and record values appear in many statistical applications and are widely used in statistical modeling and inference. Both models describe random variables arranged in order of magnitude. In addition to these well-known models, several other models of ordered random variables, known and new ones, are introduced in this book such as order statistics with non-integral sample size, sequential order statistics, k-th record values, Pfeifer' s record model, k -records from non-identical distributions and ordered random variables which arise from n truncation of distributions. These models can be effectively applied, e.g., in reliability theory. Here, an order statistic represents the life-length of some r-out-of-n-system which is an important technical structure consisting of n components. For this application, a new and more adequate model is naturally suggested. Sequential order statistics serve as a model describing certain dependencies or interactions among the system components caused by failures of components. Record values are closely connected with the occurrence times of some corresponding non-homogeneaus Poisson process and used in so lled shock models. More flexible record models, and therefore more applicable to practical situations, are considered here. The main purpose of this book is to present a concept of generalized order statistics as a unified approach to a variety of models of ordered random variables. In the distribution theoretical sense, all of the models mentioned above are contained in the proposed model of generalized order statistics.

Mixed Poisson processes are a well known class of point processes derived from (stationary) Poisson processes. In particular they cover cases where the intensity of a Poisson process is unknown but can be assumed to follow a known probability distribution. This situation is common e. g. in insurance mathematics where for instance the number of accident claims in which an individual is involved and which is evolving over some time can in principal be well described by a Poisson process with an individual, yet normally unknown intensity corresponding to the individual's accident proneness. Modelling this intensity as a random variable naturally leads to a mixed model. Usually, an insurance company will have a good estimate of the associated mixing distribution due to its large portfolio of policies.

In Kamps (1995) generalized order statistics (GOS) have been introduced as a unifying theme for several models of ascendingly ordered random variables (rv's). Following Kamps, Burkschat et al. (2003) have introduced the concept of dual generalized order statistics (DGOS) to unify several models that produce ordered rv's. The main aim of this book is to study the limit joint distribution function (df) of any two statistics in a wide subclass of the GOS and DGOS models known as m-GOS and m-DGOS respectively. This subclass contains many important practical models such as ordinary order statistics, order statistics with non-integer sample size, sequential order statistics and upper and lower record values. The limit df's of lower-lower extreme, upper-upper extreme, lower-upper extreme, central-central and lower-lower intermediate m-GOS and m-DGOS are obtained. It is revealed that the convergence of the marginals m-GOS and m-DGOS implies the convergence of the joint df. Moreover, the conditions, under which the asymptotic independence between the two marginals occurs, are derived.

This updated classic text will aid readers in understanding much of the current literature on order statistics: a flourishing field of study that is essential for any practising statistician and a vital part of the training for students in statistics. Written in a simple style that requires no advanced mathematical or statistical background, the book introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterisation results, and basic asymptotic theory. There is also a short introduction to record values and related statistics. The authors have updated the text with suggestions for further reading that may be used for self-study. Written for advanced undergraduate and graduate students in statistics and mathematics, practising statisticians, engineers, climatologists, economists, and biologists.

Finite mixture models have provided a mathematical-based approach in statistical modeling in a wide variety of random phenomena. FMM have been applied in astronomy, biology, genetic, medicine, psychiatry, economics, engineering and marketing, among many other fields in the biological, physical and social science. Kamps suggested a new theoretical approach, which is called generalized order statistics (GOS). This new model includes ordinary order statistics, sequential order statistics, progressive order statistics and record value. The main purpose of this book is to investigate the asymptotic behavior of the ordinary order statistics, generalized order statistics and dual generalized order statistics based on a random sample drawn from a finite mixture population with k components under general normalization. I obtain a sufficient conditions for this weak convergence, as well as the limit forms. Sufficient conditions are given to guarantee the existence of the weak convergence to non-degenerate distribution when the components are normalized by different normalization constants (linear-nonlinear). Illustrative examples of the most practically important distributions are obtained.