Estimation of the ratio of the scale parameters of two exponential distributions with unknown location parameters

We consider the estimation of the ratio of the scale parameters of two independent two-parameter exponential distributions with unknown location parameters. It is shown that the best affine equivariant estimator (BAEE) is inadmissible under any loss function from a large class of bowl-shaped loss functions. Two new classes of improved estimators are obtained. Some values of the risk functions of the BAEE and two improved estimators are evaluated for two particular loss functions. Our results are parallel to those of Zidek (1973, Ann. Statist., 1, 264–278), who derived a class of estimators that dominate the BAEE of the scale parameter of a two-parameter exponential distribution.     

Efficient estimation of the location parameters in the bivariate two sample problem

Summary A method of estimating the location parameter in the univariate two sample problem when one sample is reduced to a frequency distribution over a given set of class intervals has been developed by Bhattacharya [1]. In this paper we consider the problem of estimating the vector of location parameters in the bivariate two sample problem. The research reported here is an extension of Bhattacharya's work.     

A BSSS algorithm for the single facility location problem in two regions with different norms

Suppose the plane is divided by a straight line into two regions with different norms. We want to find the location of a single new facility such that the sum of the distances from the existing facilities to this point is minimized. This is in fact a non-convex optimization problem. The main difficulty is caused by finding the distances between points on different sides of the boundary line. In this paper we present a closed form solution for finding these distances. We also show that the optimal solution lies in the rectangular hull of the existing points. Based on these findings then, an efficient big square small square (BSSS) procedure is proposed.     

Comparing exponential location parameters with several controls under heteroscedasticity

Suppose that random samples are taken from \(k\) treatment groups and \(l\) control groups, where the observations in each group have a two-parameter exponential distribution. We consider the problem of constructing simultaneous confidence intervals for the differences between location parameters of the treatment groups and the control groups when the scale parameters may be unequal. Using the parametric bootstrap approach, we develop a new multiple comparisons procedure when the scale parameters and sample sizes are possibly unequal. We then present a simulation study in which we compare the performance of our proposed procedure with two other procedures. The results of our simulations indicate that our proposed procedure performs better than other procedures. The usefulness of our proposed procedure is illustrated with an example.     

Estimation of the parameters of an exponential distribution under constrained location

In this paper, the problem of estimating parameters of an exponential population is studied under quadratic loss functions. The location parameter of the distribution is assumed to be bounded above by a known constant. Various classes of estimators are derived using integral expression of risk difference (IERD) method of Kubokawa (1994). Some complete class results are also established.     

Estimation of location parameters for spherically symmetric distributions

Estimation of the location parameters of a p×1 random vector with a spherically symmetric distribution is considered under quadratic loss. The conditions of Brandwein and Strawderman [Ann. Statist. 19(1991) 1639-1650] under which estimators of the form dominate are (i) where -h is superharmonic, (ii) is nonincreasing in R, where has a uniform distribution in the sphere centered at with a radius R, and (iii) . In this paper, we not only drop their condition (ii) to show the dominance of over but also obtain a new bound for a which is sometimes better than that obtained by Brandwein and Strawderman. Specifically, the new bound of a is 0<a<[μ₁/(p²μ_-1)][1-(p-1)μ₁/(pμ_-1μ₂)]^-1 with for i=-1,1,2. The generalization to concave loss functions is also considered. Additionally, we investigate estimators of the location parameters when the scale is unknown and the observation contains a residual vector.     

Optimal selection from distributions with unknown parameters: Robustness of Bayesian models

We consider the problem of making one choice from a known number of i.i.d. alternatives. It is assumed that the distribution of the alternatives has some unknown parameter. We follow a Bayesian approach to maximize the discounted expected value of the chosen alternative minus the costs for the observations. For the case of gamma and normal distribution we investigate the sensitivity of the solution with respect to the prior distributions. Our main objective is to derive monotonicity and continuity results for the dependence on parameters of the prior distributions. Thus we prove some sort of Bayesian robustness of the model.     

On exponential rates of likelihood ratio estimators for location parameters

In this paper the exponential rates, bounds, and local exponential rates for likelihood ratio estimators are studied. Under certain regularity conditions, a family of likelihood ratio estimators is shown to be admissible in exponential rate. It is also shown that the maximum likelihood estimator is the limit of this family of estimators.     

Simultaneous testing for the successive differences of exponential location parameters under heteroscedasticity

In this paper, the design-oriented two-stage and data-analysis one-stage multiple comparison procedures for successive comparisons of exponential location parameters under heteroscedasticity are proposed. One-sided and two-sided simultaneous confidence intervals are also given. We also extend these simultaneous confidence intervals for successive differences to a larger class of contrasts of the location parameters. Upper limits of critical values are obtained using the recent techniques given in Lam [Lam, K., 1987. Subset selection of normal populations under heteroscedasticity. In: Proceedings of the Second International Advanced Seminar/Workshop on Inference Procedures Associated with Statistical Ranking and Selection, Sydney, Australia; Lam, K., 1988. An improved two-stage selection procedure. Communications in Statistics Simulation and Computation. 17 (3), 995-1006]. These approximate critical values are shown to have better results than the approximate critical values using the Bonferroni inequality developed in this paper. Finally, the application of the proposed procedures is illustrated with an example.     

Anti-synchronization in two non-identical hyperchaotic systems with known or unknown parameters

This paper considers the anti-synchronization problem between hyperchaotic Rössler system and hyperchaotic Lorenz system. Two anti-synchronization schemes of them are proposed. Active control is applied when system parameters are known and adaptive control is employed when system parameters are unknown or uncertain. Controllers and update laws of parameters are designed based on Lyapunov stability theory. In both cases, sufficient conditions for the anti-synchronization are obtained analytically. Finally, numerical simulations are provided to show the effectiveness of the proposed schemes.     

Sequential location of two facilities: comparing random to optimal location of the first facility

We review our recent results in the development of optimal algorithms for the minimization of a strictly convex quadratic function subject to separable convex inequality constraints and/or linear equality constraints. A unique feature of our algorithms is the theoretically supported bound on the rate of convergence in terms of the bounds on the spectrum of the Hessian of the cost function, independent of representation of the constraints. When applied to the class of convex QP or QPQC problems with the spectrum in a given positive interval and a sparse Hessian matrix, the algorithms enjoy optimal complexity, i.e., they can find an approximate solution at the cost that is proportional to the number of unknowns. The algorithms do not assume representation of the linear equality constraints by full rank matrices. The efficiency of our algorithms is demonstrated by the evaluation of the projection of a point to the intersection of the unit cube and unit sphere with hyperplanes.     

Unbiased estimators of p (X<Y) and their variances in the case of uniform and two-parameter exponential distributions

In this paper, we consider the two-parameter exponential distribution with unknown shift and known scale parameters as well as the uniform distribution on a segment with one end known. For these cases unbiased estimators for (1) are found. We also obtain unbiased estimators for the variances of these unbiased estimators. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 58–63, Perm, 1993.     

Minimax estimation of location parameters for certain spherically symmetric distributions

Families of minimax estimators are found for the location parameters of a p-variate distribution of the form

, where G(·) is a known c.d.f. on (0, ∞), p ≥ 3 and the loss is sum of squared errors. The estimators are of the form $\text{(1 ? ar(X′X)}\text{E}{}_0\text{(}\text{1}\text{X′X}\text{)X′X)X}$ where 0 ≤ a ≤ 2, r(X′X) is nondecreasing, and $\text{r(X′X)}\text{X′X}$ is nonincreasing. Generalized Bayes minimax estimators are found for certain G(·)'s.     

STRUCTURES OF SYSTEMS WITH EXPONENTIAL LIFE DISTRIBUTIONS

Abstract. Structures of monotone systems and cold standby systems with     

Linear square optimal control problem for stochastic difference equations with unknown parameters

The problems of stability and optimal control for stochastic difference equations are receiving important attention now (see, for example, [1–3]). In this paper, the optimal control in final form is obtained for optimal control problem of stochastic linear difference equation with unknown parameters and square cost functional. For stochastic functional differential equations, analogous result are obtained in [4].     

Chaos synchronization between two novel different hyperchaotic systems with unknown parameters

This work is devoted to investigating the synchronization between two novel different hyperchaotic systems with fully unknown parameters, i.e., an uncertain hyperchaotic Lorenz system and an uncertain hyperchaotic Lü system. Based on the Lyapunov stability theory, a new adaptive controller with parameter update law is designed to synchronize these two hyperchaotic systems asymptotically and globally. Numerical simulations are presented to verify the effectiveness of the synchronization scheme.     

The problem of obtaining prescribed distributions of gas parameters

Using one-dimensional cylindrically and spherically symmetric flows as examples, the following problem is investigated in which a prescribed density distribution in a gas is obtained: given a known gas flow (background flow), it is required to continuously attach another, unknown gas flow whose density distribution at a fixed instant of time is described by some previously given function. It is shown that this problem is a characteristic Cauchy problem of standard type for which there is a valid analogue of the Kovalevskaya theorem, provided that the input data are analytic. Other problems of the same type are considered: to ensure that the unknown flow will have a prescribed gas velocity distribution and the prescribed density of the gas in the unknown flow is strictly greater than that of the gas in the background flow (flow with discontinuity). On the assumption that the input data to these problems are analytic, the existence and uniqueness of solutions are proved, in fact—of piecewise analytic solutions. The theorems proved are extended to the case of non-one-dimensional flows.     

Generalized function projective synchronization of two different hyperchaotic systems with unknown parameters

Combining adaptive control theory with an antisymmetric structure, an extended adaptive controller which is more generalized and simpler than some existing controllers is designed. Under the controller, generalized function projective synchronization of two different uncertain hyperchaotic systems is achieved, and the unknown parameters are also estimated. In numerical simulations, the scaling function factors discussed in this paper are more complicated, and they have not been discussed in other papers. Corresponding simulation results are presented to show that the controller works well.