Mixed random systematic sampling designs

In this paper, an alternative sampling procedure that is a mixture of simple random sampling and systematic sampling is proposed. It results in uniform inclusion probabilities for all individual units and positive inclusion probabilities for all pairs of units. As a result, the proposed sampling procedure enables us to estimate the population mean unbiasedly using the ordinary sample mean, and to provide an unbiased estimator of its sampling variance. It is also found that the suggested sampling procedure performs well especially when the size of simple random sample is small. Received August 2001     

Applying lot-by-lot double sampling plan to reform Taiwan National Health Insurance auditing system

This paper seeks to design an elaborate and effective “sampling audit and payment process” for a single-payer system of national health insurance. Furthermore, contrive incentive mechanisms in the “sampling audit and payment process” to make the healthcare providers willing to apply for their medical claim payments straightforwardly. A framework of “medical claim payment auditing by double sampling plan (MCPAD)” procedure based on the lot-by-lot double sampling plan was proposed to curb the growth of medical expenses. The proposed procedure entertains several advantages, including: (1) it meets international standards of sampling plan; (2) it simplifies the auditing process; (3) it reduces sample size and auditing costs; and (4) it encourages healthcare providers using an honest medical claim payment through the incentive mechanisms. This study successfully reduces the sampling cost and effectively audits the claimed medical fees as well as encourages healthcare providers to straightforwardly apply for their medical claim payments. Practically, the proposed MCPAD procedure is also applied to healthcare provider. It is anticipated that the proposed procedure in other nations in the future.     

Is a co-jump in prices a sparse jump?

Systematic co-jumps in asset prices are generally thought to account for only a small proportion of overall jumps. In actual observations, however, jumps in asset prices are often persistent, and the time of persistence varies. In this context, we develop a new rule to identify co-jumps and improve traditional tests by considering different sampling frequencies and different sampling starting points to re-evaluate the occurrence rate of systematic co-jumps in financial assets. We conduct a simulation experiment to show that the current test procedures generally underestimate the number of co-jumps when considering persistence, but that the proposed procedure can identify co-jumps more accurately. We also perform an empirical analysis using price data from the Shanghai 50 Index and its 25 constituent stocks in China’s stock market. The average proportion of systematic co-jumps detected by the improved s-BNS is approximately 30%, which shows that the co-jump and even the systematic co-jump are not sparse jumps. The results also reveal the shortcomings of traditional jump tests in estimating persistent jumps and demonstrate that the proposed method can better detect the possible nondiversifiable risks between market indices and their constituent stocks, thereby contributing to financial risk management.     

Statistical inference on restricted partial linear regression models with partial distortion measurement errors

We consider the estimation and hypothesis testing problems for the partial linear regression models when some variables are distorted with errors by some unknown functions of commonly observable confounding variable. The proposed estimation procedure is designed to accommodate undistorted as well as distorted variables. To test a hypothesis on the parametric components, a restricted least squares estimator is proposed under the null hypothesis. Asymptotic properties for the estimators are established. A test statistic based on the difference between the residual sums of squares under the null and alternative hypotheses is proposed, and we also obtain the asymptotic properties of the test statistic. A wild bootstrap procedure is proposed to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed procedure, and a real example is analyzed for an illustration.     

发电厂入厂煤人工采样和机械采样对比试验分析

本文对发电厂入厂煤人工采样和机械采样的化验结果进行了对比。一系列检验证明,人工采样可以获得有代表性的样品。综合给出了人工采样的技术要点。     

Three‐period,two‐treatment crossover design under long‐term carryover effect

The paper describes non‐parametric approach for analysis of a three‐period, two‐treatment, four‐sequence crossover design in which test procedure for interchangeability of the treatment effects is obtained. The proposed procedure is based on a non‐parametric model, which incorporates, along with the direct treatment effects and the usual carryover effects, the long‐term carryover effects. Relevant competitors are obtained. Related asymptotic results are given. By performing simulation study, we compared the procedures with respect to type I error rate and power. Furthermore, confidence intervals for treatment differences are studied. The procedures are illustrated with a data study.     

A moment-based test for extreme-value dependence

This paper proposes a new rank-based test of extreme-value dependence. The procedure is based on the first three moments of the bivariate probability integral transform of the underlying copula. It is seen that the test statistic is asymptotically normal and its finite- and large-sample variance are calculated explicitly. Consistent plug-in estimators for the variance are proposed, and a fast algorithm for their computation is given. Although it is shown via counterexamples that no test based on the probability integral transform can be consistent, the proposed procedure achieves good power against common alternatives, both in finite samples and asymptotically.     

A comparison of the power of some tests for heteroskedasticity in the general linear model

An exact parametric test against heteroskedasticity in the general linear model is proposed. Its power is compared with that of the test proposed by Goldfeld and Quandt and with the BLUS test. Under a variety of circumstances all three tests are found to be of compare power.The proposed test is similar to the BLUS test but is based on easily computed recursive residuals and it is believed that the flexibility and computational simplicity of the new test makes it attractive as a practical procedure.     

Bayes estimates of muIti-criteria decision alternatives using Monte Carlo integration

A Bayesian procedure is proposed for the estimation of the weights of the alternatives in a multi-criteria decision model with data that stem from pair-wise comparison of alternatives. The prior information restricts the weights to the unit simplex. The posterior results are computed by Monte Carlo integration procedures based on importance sampling. The Bayesian procedure is applied to a case study concerning the choice of a professor of Operations Research (OR). Results are: (1) according to the Bayesian procedure a different candidate would be chosen as professor of OR than according to the maximum likelihood procedure; (2) given the prior and data information, there exists a substantial probability of taking the wrong decision; (3) there exists a ranking of the candidates with a posterior probability greater than one half.     

Experimental determination of the power functions of the two-sample rank tests of WILCOXON, VAN DER WAERDEN and TERRY by Monte Carlo techniques

In this paper the authors present the results of a sampling experiment to determine the power functions of the two-sample rank tests of WILCOXON, VAN DER WAERDEN and TERRY against shift alternatives for normal parent distributions, based on 2000 trials for each alternative. The sample sizes considered are m = n = 6 and m = n = 10. The powers of the three rank tests are compared with the power of the STUDENT t-test and with each other. The results indicate that in small samples (i) the power of the WILCOXON test is not much smaller than the power of the t-test and (ii) the normal scores tests are only slightly superior to the WILCOXON test, if at all.     

Response adaptive procedures with dual optimality

The paper proposes an optimal response-adaptive procedure for a general class of responses using a combination of two clinically relevant optimality criteria. We provide a thorough development for trials involving two treatments and sketch the possible extension for multiple treatments. Some related asymptotic results are worked out. We provide simulation studies to compare the performance of the proposed procedure with some of the existing competitors. We illustrate our methodology on data from a real clinical trial.     

Design considerations for neyman-pearson and wald hypothesis testing

Summary The Neyman-Pearson Lemma describes a test for two simple hypotheses that, for a given sample size, is most powerful for its level. It is usually implemented by choosing the smallest sample size that achieves a prespecified power for a fixed level. The Lemma does not describe how to select either the level or the power of the test. In the usual Wald decision-theoretic structure there exists a sampling cost function, an initial prior over the hypothesis space and various payoffs to right/wrong hypothesis selections. The optimal Wald test is a Bayes decision rule that maximizes the expected payoff net of sampling costs. This paper shows that the Wald-optimal test and the Neyman-Pearson test can be the same and how the Neyman-Pearson test, with fixed level and power, can be viewed as a Wald test subject to restrictions on the payoff vector, cost function and prior distribution.     

One-sample tests for a generalized Fréchet variance of a fuzzy random variable

A procedure to test hypotheses about the population variance of a fuzzy random variable is analyzed. The procedure is based on the theory of UH-statistics. The variance is defined in terms of a general metric to quantify the variability of the fuzzy values about its (fuzzy) mean. An asymptotic one-sample test in a wide setting is developed and a bootstrap test, which is more suitable for small and moderate sample sizes, is also studied. Moreover, the power function of the asymptotic procedure through local alternatives is analyzed. Some simulations showing the empirical behavior and consistency of both tests are carried out. Finally, some illustrative examples of the practical application of the proposed tests are presented.     

On estimating the proportion of a qualitative sensitive character using randomized response sampling

In this paper, we present results pertaining to investigations concerning randomized response sampling. We consider a simple generalization for some existing investigations, and then provide the suitable choices for design parameters. It is also demonstrated the superiority of the proposed procedure over Warner (1965) procedure.     

On a test of homogeneity for samples from a negative binomial distribution

Summary An exact test is proposed for the equality of probabilities of an event in a series of independent trials, each drawn from a negative binomial distribution with a specified number of successes. The power function of this conditional test is also derived, as well as an approximate test for homogeneity.     

A generalization of Friedman's rank statistic

Abstract  In this paper a very natural generalization of the two-way analysis of variance rank statistic of F riedman is given. The general distribution-free test procedure based on this statistic for the effect of J treatments in a random block design can be applied in general two-way layouts without interactions and with different numbers of the continuous observations per cell provided the design scheme is connected. The asymptotic distribution under the null hypothesis of the test statistic is derived. A comparison with the method of m rankings of B enard and van E lteren is made. The disadvantage of B enard and van E lteren's test procedure is that the number of observations per block does influence the statistic twice, namely firstly by the number itself, as it should, and see ondly by the level of the ranks which will be different in different blocks if the numbers of observations per block are different. The proposed test statistic is not sensitive to differences in the levels of the ranks caused by the different numbers of observations per block. The test is derived from considerhg the K ruskal -W allis statistics per block.
Finally, the results of simulation experiments are given. The simulation is carried out for three designs and a number of normal location alternatives and gives some information about the power of the suggested test procedure. A comparison is made with B enard and van E lteren's test and with the classical analysis of variance technique. For some simple orthogonal designs the exact null distributions of B enard and van E lteren's test and the proposed test are compared.     

Economic Design of EWMA Charts with Variable Sampling Intervals

Control charting is a graphical expression and operation of statistical hypothesis testing. In the present paper, we develop the economic design of the variable sampling intervals (VSI) exponentially weighted moving average (EWMA) charts to determine the values of the six test parameters of the charts (i.e., the sample size, the long sampling interval, the short sampling interval, the warning limit coefficient, the control limit coefficient, and exponential weight constant) such that the expected total cost is minimized. The genetic algorithm (GA) is applied to search for the optimal values of the six test parameters of the VSI EWMA chart, and an example is provided to illustrate the solution procedure. A sensitivity analysis is then carried out to investigate the effects of model parameters on the solution of the economic design.     

Sequential estimate for linear regression models with uncertain number of effective variables

As a result of novel data collection technologies, it is now common to encounter data in which the number of explanatory variables collected is large, while the number of variables that actually contribute to the model remains small. Thus, a method that can identify those variables with impact on the model without inferring other noneffective ones will make analysis much more efficient. Many methods are proposed to resolve the model selection problems under such circumstances, however, it is still unknown how large a sample size is sufficient to identify those “effective” variables. In this paper, we apply sequential sampling method so that the effective variables can be identified efficiently, and the sampling is stopped as soon as the “effective” variables are identified and their corresponding regression coefficients are estimated with satisfactory accuracy, which is new to sequential estimation. Both fixed and adaptive designs are considered. The asymptotic properties of estimates of the number of effective variables and their coefficients are established, and the proposed sequential estimation procedure is shown to be asymptotically optimal. Simulation studies are conducted to illustrate the performance of the proposed estimation method, and a diabetes data set is used as an example.     

Procedures to determine optimum two-stage sampling plans by attributes

Summary In order to compare two sampling plans we use the minimax regret principle, i.e. the minimax principle applied to regret functions. It is shown that among all two-stage sampling plans there exists an optimum sampling plan which can be computed with the aid of a procedure presented in this paper; furthermore another procedure is described how to obtain an approximately optimum two-stage sampling plan in a more direct way. Finally only those two-stage sampling plans are regarded which satisfy an additional condition; among these sampling plans an optimum one exists and is to be determined, too.     

基于Sieve Bootstrap方法的季节时间序列模型检验问题研究

研究目标：完善季节时间序列模型建模理论,解决建模过程烦琐、各类检验方法的结论差异大以及模型误设定问题。研究方法：基于对各季节时间序列模型的数理分析及比较,提出合理的模型检验程序;再运用Sieve Bootstrap方法,给出季节性单位根检验及确定性季节过程检验的统计量的临界值,并比较基于Sieve Bootstrap的检验方法与HEGY检验、BT检验的异同。研究发现：本文提出的检验程序能有效识别模型,检验统计量有限样本性质优良;实证分析表明,本文提出的检验程序及方法能更有效地识别中国宏观经济数据中的季节性。研究创新：将Sieve Bootstrap方法应用于季节时间序列的平稳性检验及趋势性检验中。研究价值：提出季节时间序列模型检验程序及检验方法,促进其在季节性经济数据中的应用。