Improving on two-stage estimators for scale families

Summary Consider the problem of finding an estimator for a scale parameter such that its risk function is bounded by a preassigned constant. As a solution of the problem, two-stage estimators based on only the second sample have been proposed. The paper shows that these estimators can be improved by combining the first and the second sample.     

Uniform minimum variance unbiased estimators for bivariate families

Summary We consider uniform minimum variance unbiased (UMVU) estimation of an unbiased estimable function of distribution parameters for bivariate truncation (non-regular) parameter families. In particular, we derive the UMVU estimator of the probability thatY is less thanX.     

Monotone empirical Bayes estimators for the continuous one-parameter exponential family

Abstract  A class of empirical Bayes estimators (EBE's) is proposed for estimating the natural parameter of a one-parameter exponential family. In contrast to related EBE's proposed and investigated until now, the EBE's presented in this paper possess the nice property of being monotone by construction. Based on an arbitrary reasonable estimator of the underlying marginal density, a simple algorithm is given to construct a monotone EBE. Two representations of these EBE's are given, one of which serves as a tool in establishing asymptotic results, while the other one, related with isotonic regression, proves useful in the actual computation.     

Gamma-admissibility of estimators in the one-parameter exponential family

Summary Admissibility of estimators under vague prior information on the distribution of the unknown parameter is studied which leads to the notion of gamma-admissibility. A sufficient condition for an estimator of the formδ(x)=(ax+b)/(cx+d) to be gamma-admissible in the one-parameter exponential family under squared error loss is established. As an application of this result two equalizer rules are shown to be unique gamma-minimax estimators by proving their gamma-admissibility.     

An improved empirical Bayes test for positive exponential families

We exhibit an empirical Bayes test δ^* _n for a decision problem using a linear error loss in a class of positive exponential families. This empirical Bayes test δ^* _n possesses the asymptotic optimality, and its associated regret converges to zero with rate n ⁻¹(ln n )⁶ This rate of convergence improves the previous results in the literature in the sense that a faster rate of convergence is achieved under much weaker conditions. Examples are presented to illustrate the performance of the empirical Bayes test δ^* _n     

Two-sample rank tests with truncated populations

LetX ₁,…,X _m andY ₁,…,Y _n be two independent samples from continuous distributionsF andG respectively. Using a Hoeffding (1951) type theorem, we obtain the distributions of the vector S=(S ₍₁₎,…,S _(n)), whereS _(j)=# (X _i ’s≤Y _(j)) andY _(j) is thej-th order statistic ofY sample, under three truncation models: (a)G is a left truncation ofF orG is a right truncation ofF, (b)F is a right truncation ofH andG is a left truncation ofH, whereH is some continuous distribution function, (c)G is a two tail truncation ofF. Exploiting the relation between S and the vectorR of the ranks of the order statistics of theY-sample in the pooled sample, we can obtain exact distributions of many rank tests. We use these to compare powers of the Hajek test (Hajek 1967), the Sidak Vondracek test (1957) and the Mann-Whitney-Wilcoxon test. We derive some order relations between the values of the probagility-functions under each model. Hence find that the tests based onS ₍₁₎ andS _(n) are the UMP rank tests for the alternative (a). We also find LMP rank tests under the alternatives (b) and (c).     

Optimal investment for executive stockholders with exponential utility

The scope of this paper is to enhance the model for the own-company stockholder (Desmettre et?al. in Math Methods Oper Res 72(3):347?C378, 2010), who can voluntarily performance-link his personal wealth to his management success by acquiring stocks in the own-company whose value he can directly influence via spending work effort. The executive is thereby characterized by a parameter of risk aversion and the two work effectiveness parameters inverse work productivity and disutility stress. We extend the model to a constant absolute risk aversion framework using an exponential utility/disutility setup. A closed-form solution is given for the optimal work effort an executive will apply and we derive the optimal investment strategies of the executive. Furthermore, we determine an up-front fair cash compensation applying an indifference utility rationale. Our study shows to a large extent that the results previously obtained are robust under the choice of the utility/disutility setup.     

Exponential convergence properties of linear estimators under exponential and uniform distribution

Summary Certain measures of asymptotic efficiency of test statistics are based on exponential convergence properties of the underlying error probabilities (Bahadur-, Hodges-Lehmann-efficiency). From a general large deviation theorem, that is specified to weighted sums of independent identically distributed (i.i.d.) random variables, such exponential convergence properties are derived for test statistics which are linear functions of order statistics of i.i.d. random variables under exponential and uniform distribution. For that purpose some smoothness-conditions for the weights have to be established. In a series of examples it is shown that these conditions are fulfilled for certain robust linear estimators of location or scale parameters. With the help of some numerical results two of them, namely Winsorized and trimmed mean, are compared with regard to the asymptotic relative efficiency against each other.

---

Zusammenfassung Bestimmte asymptotische Effizienzbegriffe für Tests basieren auf einem exponentiellen Konvergenzverhalten der zugrundeliegenden Fehlerwahrscheinlichkeiten (Bahadur-, Hodges-Lehmann-Effizienz). Mit Hilfe eines allgemeinen Satzes üver Wahrscheinlichkeiten großer Abweichungen, der spezialisiert wird auf gewichtete Summen unabhängiger, identisch verteilter (i.i.d.) Zufallsvariablen mit momenterzeugenden Funktionen, wird ein solches exponentielles Konvergenzverhalten nachgewiesen für Linearkombinationen von order statistics von i.i.d. Zufallsvariablen unter Exponential- und Rechteckverteilung. Dazu sind bestimmte Bedingungen an die Gewichte zu stellen. In einigen Beispielen wird gezeigt, daß solche Gewichtsbedingungen für eine Reihe von robusten Schätzern erfüllt sind. Zwei spezielle, nämlich das Winsorisierte und getrimmte Mittel, werden mit Hilfe einiger numerischer Ergebnisse hinsichtlich ihrer asymptotischen Effizienz miteinander verglichen.

     

Semiparametric econometric estimators for a truncated regression model: a review with an extension

Econometric estimators for a truncated regression model are reviewed. For each estimator, the motivations, the key assumptions, the asymptotic distribution and estimates for the asymptotic variance matrix are presented; also a new estimator is suggested. We select five practical estimators among those, and compare them through a Monte Carlo study where the response variable is simulated but the covariates are drawn from a real data set. Some practical and computational issues are addressed as well.     

Shortest confidence intervals and UMVU estimators for families of distributions involving truncation parameters

Summary In this paper, using the pivotal quantity method, new shortest-length confidence intervals and uniformly minimum variance unbiased (UMVU) estimators are constructed, where two independent random samples are available from families of distributions involving truncation parameters. Also, in the case of one sample, we give, for some uniform distributions, confidence intervals which are the shortest among all known confidence intervals.     

Nonparametric tests for Optimal Predictive Ability

A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of Optimal Predictive Ability generalizes the Superior Predictive Ability hypothesis from a single given loss function to an entire class of loss functions. Distinction is drawn between General Loss functions, Convex Loss functions, and Symmetric Convex Loss functions. The research hypothesis is formulated in terms of moment inequality conditions. The empirical moment conditions are reduced to an exact and finite system of linear inequalities based on piecewise-linear loss functions. The hypothesis can be tested in a statistically consistent way using a blockwise Empirical Likelihood Ratio test statistic. A computationally feasible test procedure computes the test statistic using Convex Optimization methods, and estimates conservative, data-dependent critical values using a majorizing chi-square limit distribution and a moment selection method. An empirical application to inflation forecasting reveals that a very large majority of thousands of forecast models are redundant, leaving predominantly Phillips Curve-type models, when convexity and symmetry are assumed.     

Percentile estimators in location-scale parameter families under absolute loss

Estimators of percentiles of location-scale parameter families are optimized based on median unbiasedness and absolute risk. Median unbiased estimators and minimum absolute risk estimators are shown to exist within a class of equivariant estimators and depend upon medians of two completely specified distributions. This work extends earlier findings to a larger class of equivariant estimators. These estimators are illustrated in the normal and exponential distributions.     

指数风险偏好下的最优保险策略

本文讨论了当投保个体和保险公司为指数风险偏好时,在保费约束下投保个体的最优保险策略问题。本文采用求解对偶优化问题的方法求解这个问题,并给出当损失服从指数分布时最优保险策略解的解析式。本文最后讨论了投保个体和保险公司风险厌恶程度以及保费预算变化对个体最优保险策略的影响。     

Distributions of exact tests in the exponential family

The aim of this paper is to give some results on the exact density of the I-divergence in the exponential family with gamma distributed observations. It is shown in particular that the I-divergence can be decomposed as a sum of two independent variables with known distributions. Since the considered I-divergence is related to the likelihood ratio statistics, we apply the method to compute the exact distribution of the likelihood ratio tests and discuss the optimality of such exact tests. One of these tests is the exact LR test of the model which is asymptotically optimal in the Bahadur sense. Numerical examples are provided to illustrate the methods discussed. Received: January 2002 Acknowledgements. I am grateful to Prof. Andrej Pázman for helpful discussions during the setup and the preparation of the paper and to the referees for constructive comments on earlier versions of the paper. Research is supported by the VEGA grant (Slovak Grant Agency) No 1/7295/20     

Goodness-of-fit tests based on series estimators in nonparametric instrumental regression

This paper proposes several tests of restricted specification in nonparametric instrumental regression. Based on series estimators, test statistics are established that allow for tests of the general model against a parametric or nonparametric specification as well as a test of exogeneity of the vector of regressors. The tests’ asymptotic distributions under correct specification are derived and their consistency against any alternative model is shown. Under a sequence of local alternative hypotheses, the asymptotic distributions of the tests are derived. Moreover, uniform consistency is established over a class of alternatives whose distance to the null hypothesis shrinks appropriately as the sample size increases. A Monte Carlo study examines finite sample performance of the test statistics.     

Regular estimators for subclasses of linear estimators

Summary The concept of regular estimator is due toRoy/Chakravarti. For its application they confined to the most general class of linear estimators. The present paper considers some subclasses of linear estimators.