共查询到20条相似文献,搜索用时 31 毫秒
1.
The paper discusses the Dudewicz-Dalal modification of a Stein-type two sample procedure for the goal of selecting the population with the largest mean from k normal populations with unknown variances. Largest k values are obtained such that a procedure based on sample means is preferred to the Dudewicz-Dalal procedure. The more general goal of choosing those populations with the t largest means is also considered. 相似文献
2.
Consider k( ? 2) normal populations whose means are all known or unknown and whose variances are unknown. Let σ2[1] ? ??? ? σ[k]2 denote the ordered variances. Our goal is to select a non empty subset of the k populations whose size is at most m(1 ? m ? k ? 1) so that the population associated with the smallest variance (called the best population) is included in the selected subset with a guaranteed minimum probability P* whenever σ2[2]/σ[1]2 ? δ* > 1, where P* and δ* are specified in advance of the experiment. Based on samples of size n from each of the populations, we propose and investigate a procedure called RBCP. We also derive some asymptotic results for our procedure. Some comparisons with an earlier available procedure are presented in terms of the average subset sizes for selected slippage configurations based on simulations. The results are illustrated by an example. 相似文献
3.
Consider k( ? 2) normal populations with unknown means μ1, …, μk, and a common known variance σ2. Let μ[1] ? ??? ? μ[k] denote the ordered μi.The populations associated with the t(1 ? t ? k ? 1) largest means are called the t best populations. Hsu and Panchapakesan (2004) proposed and investigated a procedure RHPfor selecting a non empty subset of the k populations whose size is at most m(1 ? m ? k ? t) so that at least one of the t best populations is included in the selected subset with a minimum guaranteed probability P* whenever μ[k ? t + 1] ? μ[k ? t] ? δ*, where P*?and?δ* are specified in advance of the experiment. This probability requirement is known as the indifference-zone probability requirement. In the present article, we investigate the same procedure RHP for the same goal as before but when k ? t < m ? k ? 1 so that at least one of the t best populations is included in the selected subset with a minimum guaranteed probability P* whatever be the configuration of the unknown μi. The probability requirement in this latter case is termed the subset selection probability requirement. Santner (1976) proposed and investigated a different procedure (RS) based on samples of size n from each of the populations, considering both cases, 1 ? m ? k ? t and k ? t < m ? k. The special case of t = 1 was earlier studied by Gupta and Santner (1973) and Hsu and Panchapakesan (2002) for their respective procedures. 相似文献
4.
Consider k independent observations Yi (i= 1,., k) from two-parameter exponential populations i with location parameters μ and the same scale parameter If the μi are ranked as consider population as the “worst” population and IIp(k) as the “best” population (with some tagging so that p{) and p(k) are well defined in the case of equalities). If the Yi are ranked as we consider the procedure, “Select provided YR(k) Yr(k) is sufficiently large so that is demonstrably better than the other populations.” A similar procedure is studied for selecting the “demonstrably worst” population. 相似文献
5.
In multivariate stratified sample survey with L strata, let p-characteristics are defined on each unit of the population. To estimate the unknown p-population means of each characteristic, a random sample is taken out from the population. In multivariate stratified sample survey, the optimum allocation of any characteristic may not be optimum for others. Thus the problem arises to find out an allocation which may be optimum for all characteristics in some sense. Therefore a compromise criterion is needed to workout such allocation. In this paper, the procedure of estimation of p-population means is discussed in the presence of nonresponse when the use of linear cost function is not advisable. A solution procedure is suggested by using lexicographic goal programming problem. The numerical illustrations are given for its practical utility. 相似文献
6.
A procedure for selecting a Poisson population with smallest mean is considered using an indifference zone approach. The objective is to determine the smallest sample size n required from k ≥ 2 populations in order to attain the desired probability of correct selection. Since the means procedure is not consistent with respect to the difference or ratio alone, two distance measures are used simultaneously to overcome the difficulty in obtaining the smallest probability of correct selection that is greater than some specified limit. The constants required to determine n are computed and tabulated. The asymptotic results are derived using a normal approximation. A comparison with the exact results indicates that the proposed approximation works well. Only in the extreme cases small increases in n are observed. An example of industrial accident data is used to illustrate this procedure. 相似文献
7.
A review of the randomized response model introduced by Warner (1965) is given, then a randomized response model applicable to continuous data that considers a mixture of two normal distributions is considered. The target here is not to estimate any parameter, but rather to select the population with the best parameter value. This article provides a study on how to choose the best population between k distinct populations using an indifference-zone procedure. Also, this article includes tables for the required sample size needed in order to have a probability of correct selection higher than some specified value in the preference zone for the randomized response model considered. 相似文献
8.
We investigate the construction of a BCa-type bootstrap procedure for setting approximate prediction intervals for an efficient estimator θm of a scalar parameter θ, based on a future sample of size m. The results are also extended to nonparametric situations, which can be used to form bootstrap prediction intervals for a large class of statistics. These intervals are transformation-respecting and range-preserving. The asymptotic performance of our procedure is assessed by allowing both the past and future sample sizes to tend to infinity. The resulting intervals are then shown to be second-order correct and second-order accurate. These second-order properties are established in terms of min(m, n), and not the past sample size n alone. 相似文献
9.
Yoshikazu Takada 《Revue canadienne de statistique》1981,9(2):225-228
This paper deals with obtaining an upper tolerance limit for a largest observation X(n) in an ordered sample of size n from a continuous distribution where the first m observations X(1) < X(2) < … < X(m), l ≤ m < n, have been observed. A criterion of “goodness” of tolerance limit is developed, and a method is given to obtain the best tolerance limit. This method is applied to exponential and Pareto distributions. 相似文献
10.
《Journal of Statistical Computation and Simulation》2012,82(4):337-349
In this paper we examine the failure-censored sampling plans for the two–parameter exponential distri- bution based on m random samples, each of size n. The suggested procedure is based on exact results and only the first failure time of each sample is needed. The values of the acceptability constant are also tabulated for selected values of p α 1 p β 1, α and β. Further, a comparison of the proposed sampling plans with ordinary sampling plans using a sample of size mn is made. When compared to ordinary sampling plans, the proposed plan has an advantage in terms of shorter test-time and a saving of resources. 相似文献
11.
Eve Bofinger 《统计学通讯:理论与方法》2013,42(5):1697-1716
Consider sample means from k(≥2) normal populations where the variances and sample sizes are equal. The problem is to find the ‘least significant difference’ or ‘spacing’ (LSS) between the two largest means, so that if an observed spacing is larger we have confidence 1 - α that the population with largest sample mean also has the largest population mean. When the variance is known it is shown that the maximum LSS occurs when k = 2, provided a < .2723. In other words, for any value of k we may use the usual (one-tailed) least significant difference to demonstrate that one population has a population mean greater than (or equal to) the rest. When the variance is estimated bounds are obtained for the confidence which indicate that this last result is approximately correct. 相似文献
12.
A control procedure is presented for monitoring changes in variation for a multivariate normal process in a Phase II operation where the subgroup size, m, is less than p, the number of variates. The methodology is based on a form of Wilk' statistic, which can be expressed as a function of the ratio of the determinants of two separate estimates of the covariance matrix. One estimate is based on the historical data set from Phase I and the other is based on an augmented data set including new data obtained in Phase II. The proposed statistic is shown to be distributed as the product of independent beta distributions that can be approximated using either a chi-square or F-distribution. An ARL study of the statistic is presented for a range of conditions for the population covariance matrix. Cases are considered where a p-variate process is being monitored using a sample of m observations per subgroup and m < p. Data from an industrial multivariate process is used to illustrate the proposed technique. 相似文献
13.
The problem of selecting the normal population with the largest population mean when the populations have a common known variance is considered. A two-stage procedure is proposed which guarantees the same probability requirement using the indifference-zone approach as does the single-stage procedure of Bechhofer (1954). The two-stage procedure has the highly desirable property that the expected total number of observations required by the procedure is always less than the total number of observations required by the corresponding single-stage procedure, regardless of the configuration of the population means. The saving in expected total number of observations can be substantial, particularly when the configuration of the population means is favorable to the experimenter. The saving is accomplished by screening out “non-contending” populations in the first stage, and concentrating sampling only on “contending” populations in the second stage. The two-stage procedure can be regarded as a composite one which uses a screening subset-type approach (Gupta (1956), (1965)) in the first stage, and an indifference-zone approach (Bechhofer (1954)) applied to all populations retained in the selected sub-set in the second stage. Constants to implement the procedure for various k and P? are provided, as are calculations giving the saving in expected total sample size if the two-stage procedure is used in place of the corresponding single-stage procedure. 相似文献
14.
Consider k (≥ 2) independent exponential populations with different location and scale parameters. Call a population associated with largest of unknown location parameters as the best population. For the goal of selecting the best population, it is established that if the scale parameters are completely unknown, then the indifference-zone probability requirement can not be guaranteed by any single sample decision rule which is just and translation invariant. Under the assumption that the scale parameters are bounded above by a known constant, a single sample selection procedure is proposed for which the indifference-zone probability requirement can be guaranteed. Under the same assumption, 100P*% simultaneous upper confidence intervals for all distances from the largest location parameter are also obtained. 相似文献
15.
《统计学通讯:模拟与计算》2013,42(3):357-373
ABSTRACT This paper studies the asymptotic distribution of the largest eigenvalue of the sample covariance matrix. The multivariate distribution for the population is assumed to be elliptical with finite kurtosis 3κ. An expression as an expectation is obtained for the distribution function of the largest eigenvalue regardless of the multiplicity, m, of the population's largest eigenvalue. The asymptotic distribution function and density function are evaluated numerically for m = 2,3,4,5. The bootstrap of the average of the m largest eigenvalues is shown to be consistent for any underlying distribution with finite fourth-order cumulants. 相似文献
16.
Mohanad Fayez Al-khasawneh 《Journal of Statistical Computation and Simulation》2019,89(12):2175-2186
In this article, we consider the Bayes and empirical Bayes problem of the current population mean of a finite population when the sample data is available from other similar (m-1) finite populations. We investigate a general class of linear estimators and obtain the optimal linear Bayes estimator of the finite population mean under a squared error loss function that considered the cost of sampling. The optimal linear Bayes estimator and the sample size are obtained as a function of the parameters of the prior distribution. The corresponding empirical Bayes estimates are obtained by replacing the unknown hyperparameters with their respective consistent estimates. A Monte Carlo study is conducted to evaluate the performance of the proposed empirical Bayes procedure. 相似文献
17.
Three tests are considered concerning the common mean of two normal populations: (1) an F test based on a sample from one population, (2) a test based on the addition of the F statistics from independent samples from two popultions (proposed), and (3) a test based on the maximum of the F statistics from two independent samples from two populations. A condition under which test (2) is locally more powerful than test (1) is given. As the test statistic in test (2) does not follow a standard distribution, a formula for approximating the observed significance level is provided. A simulation study is used to compare the power of these tests. 相似文献
18.
Hubert J. Chen 《统计学通讯:理论与方法》2013,42(8):851-864
Let π1…, πk denote k(≥ 2) populations with unknown means μ1 , …, μk and variances σ1 2 , …, σk 2 , respectively and let πo denote the control population having mean μo and variance σo 2 . It is assumed that these populations are normally distributed with correlation matrix {ρij}. The goal is to select a subset, of populations of π1 , …, πk which contains all the populations with means larger than or equal to the mean of the control one. Procedures are given for selecting such a subset so that the probability that all the populations with means larger than or equal to the mean of the control one are included in the selected subset is at least equal to a predetermined value P?(l/k < P? < 1). The goal treated here is a first step screening procedure that allows the experimenter to choose a subset and withhold judgement about which one has the largest mean. Then, if the one with the largest mean is desired it can be chosen from the selected subset on the basis of cost and other considerations. Percentage points are also included. 相似文献
19.
In this article, we discuss a two-stage procedure for selecting the largest location parameter among k(k≥2) two-parameter exponential populations(or products) from an accelerated test. The accelerated test will be conducted at a higher stress level than that of normal in the second stage. under certain assumptions between parameter and stress leveL, the two-stage selection procedure, which guarantees that the probability of correct selection is at least p*, is proposed. At the end of the paper , we present some useful tables that serve as a guide for the needed sample size in the second stage. 相似文献
20.
i
, i = 1, 2, ..., k be k independent exponential populations with different unknown location parameters θ
i
, i = 1, 2, ..., k and common known scale parameter σ. Let Y
i
denote the smallest observation based on a random sample of size n from the i-th population. Suppose a subset of the given k population is selected using the subset selection procedure according to which the population π
i
is selected iff Y
i
≥Y
(1)−d, where Y
(1) is the largest of the Y
i
's and d is some suitable constant. The estimation of the location parameters associated with the selected populations is considered
for the squared error loss. It is observed that the natural estimator dominates the unbiased estimator. It is also shown that
the natural estimator itself is inadmissible and a class of improved estimators that dominate the natural estimator is obtained.
The improved estimators are consistent and their risks are shown to be O(kn
−2). As a special case, we obtain the coresponding results for the estimation of θ(1), the parameter associated with Y
(1).
Received: January 6, 1998; revised version: July 11, 2000 相似文献