Improved stein-rule estimator for regression problems

Stein-Rule estimator for regression problems has been studied by several authors including Sclove (1968) and others listed in Vinod's (1978) survey. Ullah and Ullah (1978) provide the expressions for the mean squared error (MSE) of a double k-class (KK) estimator with parameters k₁ and k₂. When k₂=1 the Stein-Rule estimator becomes a special case of KK and an optimal choice of k₁ is known. This paper explores optimal theoretical choice of k₁ and k₂. We note that negative choices of k₂ are permissible, and that thereis a large range of choices for K₁ and k₂ where the MSE of the Stein-Rule estimator can be reduced for regression problems based on multicollinear data. A simulation experiment is included.     

Revelations of a gambler

Knowing that a decision maker maximizes expected utility with respect to some (unknown) utility U and some (unknown) probability P, what can one tell about P by observing his decisions? We discuss this revealed preference question primarily in the simple case of a two-element (H and T) state space, and show that the possible revelations of P_T/P_H are precisely those of the form P_T/P_Hε∪^K_k=1 (γ_k,δ_k), for some algebraic numbers γ_k,δ_k.     

On theE-optimality of certain asymmetrical designs under mixed effects model

TheE-optimality of the following designs within the class of all proper and connected designs with givenb, k andv under mixed effects model are established.

1. A group divisible design with λ₂ = λ₁ + 1.
2. A group divisible design with λ₁ = λ₂ + 1 and group size 2.
3. A linked block design.
4. The dual of design (i)
5. The dual of design (ii).

All these designs are known to satisfy the same optimality property under fixed effects model whenk<v, while the design (i) is known to beE-optimal even whenk>v. From the results proved here, theE-optimality of designs (ii, (iii), (iv) and (v) under fixed effects model in the situation whenk >v also follows.     

Design of optimal step�Cstress accelerated life tests under progressive type I censoring with random removals

This paper considers the analysis of exponential distributed lifetime data observed under k-stage step?Cstress accelerated life test under progressive type I censoring with random removal (PCRR), where the number of units removed at each stress follows a binomial distribution. We compute the expected Fisher information matrix of the maximum likelihood estimator of the log mean life at design (use) stress. The problem of choosing the optimal time ?? _i , i = 1, . . . , k ? 1 under k-stage step?Cstress is addressed using variance (V)-optimality as well as determinant (D)-optimality criteria. An illustrative example is provided with discussion.     

Estimation in the first-order moving average model through the finite autoregressive approximation: Some asymptotic results

To estimate α in the model y_t = u_t+αu_t?1, we consider a proposal by Durbin (Biometrika, 1969). It consists in fitting an autoregression of order k to the data, and deriving from there an estimate α^. The probability limit and the variance of the limiting normal distribution of α^ are presented and discussed in detail, when the sample size T → ∞, but k remains fixed. The differences between the resulting values and those corresponding to the maximum likelihood estimator are exponentially decreasing functions of k. Several modifications of the estimator are discussed and found consistent, but asymptotically inefficient.     

On the product of gamma random variables

We give expressions for the distribution and density of a product of gamma or equivalently chi-square random variables. In particular, we give the distribution of the product of two independent gamma variables of mean k in terms of the Bessel functions K ₁, … , K _k .     

Berry-Esséen rate in asymptotic normality for perturbed sample quantiles

In estimating quantiles with a sample of sizeN obtained from a distributionF, the perturbed sample quantiles based on a kernel functionk have been investigated by many authors. It is well known that their behaviour depends on the choices of “window-width”, sayw _N. Under suitable and reasonably mild assumptions onF andk, Ralescu and Sun (1993) have recently proven that lim _N→∞ N ^1/4w_N=0 is the necessary and sufficient condition for the asymptotic normality of the perturbed sample quantiles. In this paper, their rate of convergence is investigated. It turns out that the optimal Berry-Esséen rate ofO(N^?1/2) can be achieved by choosing the window-width suitably, sayw _N=O(N^?1/2). The obtained results, in addition to being explicit enough to verify the sufficient condition for the asymptotic normality, improve Ralescu's (1992) result of which the rate is of order (logN)N ^?1/2.     

On nonparametric latent trait models

Some nonparametric latent trait models for dichotomous data are considered. We deal with n subjects, each answering to the same set of of k items, each item being scored dichotomously. We are interested in ordering item difficulties α₁,...α_k . In Sec. 2 it is shown that in the considered nonparametric models the ordering is identifiable. Then an order estimator is defined and its quality is described by the probabilities of correct, wrong and deferred decision. Asymptotic behaviour of these probabilities are considered for n→∞ and any k≥2. The hypothesis that the probability of wrong decision diminishes when the model is “more distant” from so called random response model, is proved for n≤3 and verified numerically for n≥3. In Sec. 4 we discuss critically some parameters of nonparametric models known in the literature as “coefficients of scalability”. In particular, for k=2 their connections with the evaluation of positive dependence are considered.     

Utility functions for Debreu's ‘excess demands’

Given an arbitrary function x: $\text{R}$^l→$\text{R}$^l satisfying Walras law and homogeneity, Debreu decomposed x into the sum of l ‘individually rational’ functions x(p)=Σ^l_k=1[uvbar|x]^k(p). Here we find explicit utility functions u^k, constructed on the basis of a simple geometric intuition, which give rise to Debreu's excess demands [uvbar|x]^k(p).     

What Is the Economic Meaning of FDH?

The central feature of the FDH model is the lack of convexity for its production possibility set, TF. Starting with n observed (distinct) decision making units DMU_k , each defined by an input-output vector p _k = [y _k -x _k], domination is defined by ordinary vector inequalities. DMU_k is said to dominate DMU_j if p _k ≥ p _j , p _k ≠ p _j . The FDH production possibility set TF consists of the observed DMU_j together with all input-output vectors p=[y_k,?x_k] with y ≥ 0, x ≥ 0, y ≠ 0, x ≠ 0 which are dominated by at least one of the observed DMU_j. DMU_k is defined as “FDH efficient” if no DMU_j dominates it. In the BCC (or variable return to scale) DEA model the production possibility set TB consists of the observed DMU_k together with all input-output vectors dominated by any convex combination of them and DMU_k is DEA efficient if it is not dominated by any p in TB. In the DEA model, economic meaning is established by the introduction of (non negative) multiplier (price) vectors w = [u,v]. If DMU_k is undominated (in TB) then there exists a positive multiplier vector w for which (a) w ^T p _k = u ^T y _k ? v ^T x _k ≥ w ^T p for every p ∈ TB. In everyday language, the net return (or profit) for DMU_k relative to the given multiplier vector w is at least as great as that for any production possibility p. On the other hand, if DMU_k is FDH but not DEA efficient then it is proved that there exists no positive multiplier vector >w for which (a) holds, i.e. for any positive w there exists at least one DMU_j for which w ^T p _j > w^T p _k . Since, therefore, FDH efficiency does not guarantee price efficiency what is its economic significance? Without economic significance, how can FDH be considered as being more than a mathematical system however logically soundly it may be conceived?

     

A note on moments of k-class estimators for negative k

The validity of expressions for the exact moments of k-class estimator with 0≦1E;k<1 is established for negative values of k in the interval (–1,0). For other negative values (–∞<k≦1E;–1) the derivation of expressions for moments is outlined.     

Exact finite sample properties of double k-class estimators in simultaneous equations

In our earlier paper [Srivastava, Agnihotri and Dwivedi (1980)] the dominance of double k-class over k-class with respect to exact mean squared error matrix criteria is established. It is observed that given a member of k-class, one can pick up a member of double k-class that will provide an improved estimator of the coefficients. This result prompted us to study the exact finite sample properties of the double k-class estimator. For this, we have considered a structural equation containing two endogenous variables and have investigated the properties of double k-class estimators of the coefficients of explanatory endogenous variables assuming characterizing scalars to be non-stochastic.     

The graph of the k-class estimator: An algebraic and statistical interpretation

This paper presents an algebraic analysis of the graphs of the k-class estimator, its asymptotic standard error and asymptotic t-ratio as functions of k for a single structural equation containing one or more endogenous explanatory variables. These results are illustrated by the corresponding graphs of the second and fifth equations of the Girshick-Haavelmo (1947) Demand for Food Model.Tests of the rank condition for identification are also developed. They are found to involve the values of k which explode the k-class estimator.     

Feedback orientation: A meta-analysis

Feedback orientation reflects an individual difference in one's receptivity to feedback. We present the results of a meta-analysis of the feedback orientation literature. Based on k = 46 independent samples, representing n = 12,478 workers, meta-analytic results suggest that feedback orientation is positively related to learning goal orientation (r_c = 0.39), job satisfaction (r_c = 0.33), work performance (r_c = 0.35), and feedback seeking (r_c = 0.43). Meta-analytic regression and dominance analysis was used to tease apart how related informal feedback constructs (i.e., feedback seeking, feedback environment, & feedback orientation) aid in the prediction of outcomes, above and beyond two established predictors of job attitudes and work performance: role clarity and leader-member exchange. We also present an interactive exploratory data analysis tool to aid in developing future research questions regarding the connection between informal feedback constructs and work outcomes.     

K-matrix-class estimators and the full-information maximum-likelihood estimator as a special case

This article presents a unified treatment of simultaneous system estimation. A general class of full-information estimators is proposed, called K-matrix-class (KMC). It is shown that the K-matrix-class includes both full-information maximum-likelihood and three-stage least- squares estimators as special cases and that the k-class can be regarded as a subclass of the K-matrix-class. Conditions under which KMC estimators are consistent (similar to those of the k-class estimators) are given. Furthermore, as a full information-generalization of the double k-class estimators, the double K-matrix-class estimators (DKMC) are proposed.     

Characterization of the exponential distribution function by Properties of the differenceX k+s∶n−X k∶n of order statistics

We considern independent and identically distributed random variables with common continuous distribution functionF concentrated on (0, ∞). LetX _1∶n≤X_2∶n...≤X_n∶n be the corresponding order statistics. Put $$d_s \left( x \right) = P\left( {X_{k + s:n} - X_{k:n} \geqslant x} \right) - P\left( {X_{s:n - k} \geqslant x} \right), x \geqslant 0,$$ and $$\delta _s \left( {x, \rho } \right) = P\left( {X_{k + s:n} - X_{k:n} \geqslant x} \right) - e^{ - \rho \left( {n - k} \right)x} ,\rho > 0,x \geqslant 0.$$ Fors=1 it is well known that each of the conditions d₁(x)=O ?x≥0 and δ₁ (x, p) = O ?x≥0 implies thatF is exponential; but the analytic tools in the proofs of these two statements are radically different. In contrast to this in the present paper we present a rather elementary method which permits us to derive the above conclusions for somes, 1≤n —k, using only asymptotic assumptions (either forx→0 orx→∞) ond _s(x) and δ₁ (x, p), respectively.     

Berry-Esseen-type inequality for a special class of distributions

LetQ be the distribution of the suitably normalized sum of i. i. d.k-dimensional random vectors (k2) and letf be a measurable real valued function of the formf(z ₁,...,z _k )=z ₁+r(z ₂,...,z _k ), where the measurable functionr fulfills certain regularity conditions. A Berry-Esseen-type inequality is derived for the one-dimensional distributionP=Qf ^–1.     

Una successione di approssimanti per difetto della probabilita' di rovina

In[1] abbiamo stabilito un confine inferiore della probabilità di rovina ψ (u) cioè, psstoR=1-ψ si è mostrato che (*) $$R(u) \leqslant \frac{{1 - \tfrac{a}{c}}}{{1 - \tfrac{{a\mu }}{c}\int_0^u {(1 - F(x)) d x} }} = R(0)k_1 (u), u \geqslant 0$$ dovele costantia, c e μ e la funzioneF hanno il significato che è precisato nel seguito. Noi mostreremo l'esistenza, nella classeC _b delle funzioni continue e limitate, della soluzione dell'equazione integrale (2), cui soddisfa la funzioneR, prescindendo dalla continuità dell'operatore associato all'equazione, ma facendo ricorso soltanto a condizioni di monotonia e alla scelta diR(0)k ₁ quale funzione da cui fare partire il processo iterativo; processo che, ad ogni stadio, porge un confine superiore diR migliore del precedente, e che converge uniformemente sui limitati di [0,+∞) verso la soluzione di (2).     

A note on reliability properties of k-record statistics

LetY _k,n denote the nth (upper) k-record value of an infinite sequence of independent, identically distributed random variables with common continuous distribution function F. We show that if the nth k-record valueY _k,n has an increasing failure rate (IFR), thenY _l,n (l<k) andY _k+1,n+1(n≤k+1) also have IFR distributions. On the other hand, ifY _k,n has a decreasing failure rate (DFR), thenY _l,n (1>k) has also a DFR distribution. We also present some results concerning log convexity and log concavity ofY _k,n .