We propose a multivariate extreme value threshold model for joint tail estimation which overcomes the problems encountered with existing techniques when the variables are near independence. We examine inference under the model and develop tests for independence of extremes of the marginal variables, both when the thresholds are fixed, and when they increase with the sample size. Motivated by results obtained from this model, we give a new and widely applicable characterisation of dependence in the joint tail which includes existing models as special cases. A new parameter which governs the form of dependence is of fundamental importance to this characterisation. By estimating this parameter, we develop a diagnostic test which assesses the applicability of bivariate extreme value joint tail models. The methods are demonstrated through simulation and by analysing two previously published data sets.

COWLES FOUNDATION DISCUSSION PAPER NO. 1229

This paper presents a covariance function analysis of mature weight records of beef cows, fitting a random regression model. It attempts to demonstrate the kind of calculations, results and problems which might be encountered with this `new' type of analysis, but throughout the paper, familiarity with covariance function models is assumed. Material and methods

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and�or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: Ž. i quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and Ž ii. LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.

Experimental data have consistently shown diversity in beliefs as well as in actions among experimental subjects. This paper presents and compares alternative behavioral econometric models for the characterization of player heterogeneity, both between sub-populations of players and within subpopulations. In particular, two econometric models of diversity within sub-populations of players are investigated: one using a model of computational errors and the other allowing for diversity in prior beliefs around a modal prior for the sub-population.

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Abstract. This article reviews key contributions in the area of statistics as applied to the use of molecular marker technology and quantitative genetics in the search for genes affecting quantitative traits responsible for specific diseases and economically important agronomic traits. Since an exhaustive literature review is not possible, the limited scope of this work is to encourage further statistical work in this vast field by first reviewing human and domestic species literature, and then concentrating on the statistical developments for experimental breeding populations. Substantial gains have been made over the years by both plant and animal breeders toward a long-term goal of locating genes affecting quantitative traits (quantitative trait loci, QTLs) for the eventual characterization and manipulation of these genes in order to develop improved agronomically important traits. Our main concern is that the care and expense that are required in generating both genetic marker data and quantitative trait data should be accompanied by equal care in the statistical analysis of the data. Through an example using an F 2 male genetic map of mouse chromosome 10, and quantitative trait values measured on weight gain, we implement much of the reviewed methodology for the purpose of detecting or locating a QTL having an effect on weight gain. Key words and phrases: Interval mapping; interval testing; multiple markers; mixture distribution; QTL; single markers. 1.

We explain how to apply statistical techniques to solve several language-recognition problems that arise in cryptanalysis and other domains. Language recognition is important in cryptanalysis because, among other applications, an exhaustive key search of any cryptosystem from ciphertext alone requires a test that recognizes valid plaintext. Written for cryptanalysts, this guide should also be helpful to others as an introduction to statistical inference on Markov chains. Modeling language as a finite stationary Markov process, we adapt a statistical model of pattern recognition to language recognition. Within this framework we consider four welldefined language-recognition problems: 1) recognizing a known language, 2) distinguishing a known language from uniform noise, 3) distinguishing unknown 0th-order noise from unknown 1st-order language, and 4) detecting non-uniform unknown language. For the second problem we give a most powerful test based on the Neyman-Pearson Lemma. For the oth...

this paper but the results were practically indistinguishable, indicating that our choice of grid provides an accurate approximation

Abstract. We introduce estimation and test procedures through divergence optimization for discrete or continuous parametric models. This approach is based on a new dual representation for divergences. We treat point estimation and tests for simple and composite hypotheses, extending maximum likelihood technique. An other view at the maximum likelihood approach, for estimation and test, is given. We prove existence and consistency of the proposed estimates. The limit laws of the estimates and test statistics (including the generalized likelihood ratio one) are given both under the null and the alternative hypotheses, and approximation of the power functions is deduced. A new procedure of construction of confidence regions, when the parameter may be a boundary value of the parameter space, is proposed. Also, a solution to the irregularity problem of the generalized likelihood ratio test pertaining to the number of components in a mixture is given, and a new test is proposed, based on χ 2-divergence on signed finite measures and duality technique.