Small Area Estimation for Zero-Inflated Data

The commonly used method of small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). The authors discuss the SAE for zero-inflated data under a two-part random effects model that account for excess zeros in the data. Empirical results show that proposed method for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Office of India demonstrates the satisfactory performance of the method. The authors describe a parametric bootstrap method to estimate the mean squared error (MSE) of the proposed estimator of small areas. The bootstrap estimates of the MSE are compared to the true MSE in simulation study.     

Shortest Expected Length Confidence Interval for the Power of the t-Test

Tarasińska (2005 Tarasińska , J. ( 2005 ). Confidence intervals for the power of student's t-test . Statistics & Probability Letters 73 ( 2 ): 125 – 130 .[Crossref], [Web of Science ®] , [Google Scholar]) considered a method to construct the shortest length confidence interval on the power of the t-test using a confidence interval for the population standard deviation in the non centrality parameter. Gilliland and Li (2008 Gilliland , D. , Li , M. ( 2008 ). A note on confidence intervals for the power of t-test . Statistics & Probability Letters 78 ( 5 ): 488 – 489 .[Crossref], [Web of Science ®] , [Google Scholar]) used simulations to show that this confidence interval has less than the nominal coverage, particularly in small samples. We propose to find the shortest expected length confidence interval for the power of the t-test by accounting for the variation in the sample standard deviation, and provide the necessary constants for its implementation for some selected sample and shift sizes. It is seen that the proposed interval is reasonably robust to the specification of the population standard deviation and maintains the nominal coverage.     

Sample Size Calculation for Comparing Two Normal Random Samples Using Equilibrium Priors

A method of sample size calculation for deciding whether the difference of the expectations of two normal random samples is positive or negative is introduced. Prior information is used in the form of a set of plausible prior distributions and plausible loss functions for the two kinds of incorrect decisions. The sample size is set so that the prior-related probability of concluding the analysis with an impasse is smaller than a given threshold or by minimizing the total of the cost of the planned experiment and the expected losses due to an incorrect decision or an inconclusive outcome of the analysis.     

A modified multiple comparisons with a control for exponential location parameters based on doubly censored sample under heteroscedasticity

ABSTRACT

In this paper, a modified one-stage multiple comparison procedures with a control for exponential location parameters based on the doubly censored sample under heteroscedasticity is proposed. A simulation study is done and the results show that the proposed procedures have shorter confidence length with coverage probabilities closer to the nominal ones compared with the one proposed in Wu (2017 Wu, S. F. 2017. Multiple comparisons of exponential location parameters with a control based on doubly censored sample under heteroscedasticity. Communications in Statistics: Simulation and Computation 46 (3):1858–1870. doi: 10.1080/03610918.2015.1017582.[Taylor & Francis Online] , [Google Scholar]). At last, an example of comparing the duration of remission for four drugs as the treatment of leukemia is given to demonstrate the proposed procedures.     

Updating a discriminant function on the basis of unclassified data

The problem of updating a discriminant function on the basis of data of unknown origin is studied. There are observations of known origin from each of the underlying populations, and subsequently there is available a limited number of unclassified observations assumed to have been drawn from a mixture of the underlying populations. A sample discriminant function can be formed initially from the classified data. The question of whether the subsequent updating of this discriminant function on the basis of the unclassified data produces a reduction in the error rate of sufficient magnitude to warrant the computational effort is considered by carrying out a series of Monte Carlo experiments. The simulation results are contrasted with available asymptotic results.     

A comparison of methods for studentizing the sample median

Various methods for “Studentizing” the sample median are com-pared on the basis of a Monte Carlo study. Several of the methods do rather poorly while two, the bootstrap and the standardized length of a distribution free confidence interval, behave accept-ably acrors a wide range of sample sizes and several distributions of varying tail length. These two methods seem to agree closely with the distribution free confidence intervals and moreover, un-like these intervals, the methods can be extended to a method of accurate inference for λ₁ regreasion.     

The power of the fisher permutation test in 2 × k tables

The power of the Fisher permutation test extended to 2 × k tables is evaluated unconditionally as a function of the under-lying cell probabilities in the table. These results are then applied in assessing the sensitivity of two-generation cancer bioassays in which a fixed number of pups from each litter born in the first generation are selected to continue on test in the second generation. In this case, the two rows of the table correspond to two treatment groups and the k columns correspond to the number of animals responding in a litter. The cell probabilities in this application are based on a suitable beta-binomial superpopulation model.     

Repeated measurement analysis for nonnormal data in small samples

The small sample performance of Zeger and Liang's extended generalized linear models for the analysis of longitudinal data (Biometrics, 42,121-130,1986) is investigated for correlated gamma data. Results show that the confidence intervals do not provide desirable coverage of the true parameter due to considerably biased point estimates. Improved estimates are proposed using the jackknife procedure. Simulations performed to evaluate the proposed estimates indicate superior properties to the previous estimates.     

One sample tests of location for nonnormal symmetric data

A score test of location is derived for data from a distorted normal distribution. A simulation study compares the performance of this test to the t-test and Wilcoxon test for symmetric data from such a distribution. For this type of data the score test can be considerably more powerful than both the t-test and Wilcoxon test. This suggests that such a score test may be useful in practice when variations from normality can be modeled by such a family of distributions.     

On the evaluation of steck's determinant for rectangle probabilities of uniform order statistics

An expeditious method is developed for systematically evaluating Steck’s determinant for rectangle probabilities of uniform order statistics as polynomials generated by a binary tree.