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The idea of this special issue was to gather a number of papers on various aspects of theoretical works supported mostly with applications and simulations from the presenters of the conference. This conference brought together for 4 days distinguished international statistical researchers, prominent industrial practitioners, and new researchers and students to Bodrum, Turkey, to discuss new developments and methods in statistical science. We would like to thank all who kindly contributed their papers for this issue, and we especially thank Professor N. Balakrishnan for making this special issue a reality for participants of ISDC2018 and the journal editorial assistant, Ms. Debbie Iscoe, for her kind help and co-operation with the processing and production issues. [Extracted from the article]

The strong limit theory is one of the most important problems in the probability theory. Some results on the convergence of ρ ˜ -mixing random sequences have been presented. In this paper, we study the almost sure convergence for ρ ˜ -mixing random sequences. As a result, we generalize partial results of Wu and Jiang [Wu, Q. Y., and Y. Y. Jiang. 2008. Some strong limit theorems for q ˜ -mixing sequences of random variables. Statistics & Probability Letters 78:1017–23.]. We obtain the mainstream and some corresponding conclusions by using truncation methods and the generalized three-series theorem. A known result is generalized in this paper. [ABSTRACT FROM AUTHOR]

The article offers information on the professional career and contributions of Professor Masafumi Akahira of the University of Tsukuba, Ibaraki, Japan. As stated, he was a research associate at the University of Electro Communications (UEC) in Tokyo from 1971 to 1976. He contributed a lot in the field of statistics which includes research on second and third order asymptotic efficiencies of estimators by the use of edgeworth expansion. A list of his publications is also presented.

The shape of a statistical distribution of data is important in such diverse areas as descriptive analysis, risk analysis, portfolio optimization and strategic decision making. If it is known (or believed) that the probability density function of a random variable is not symmetric, the question of its skewness becomes important. Various methods of assessing skewness have been formulated, but none are totally satisfactory. The classical measurement of skewness is based on higher moments of the random variable about its mean. However, it is well known that those measurements are sensitive to extreme outliers. The implication of the sensitivity is that mean-based metrics of skewness are inefficient, especially in small or medium size sample data. Those mean-based metrics are known as Pearson skewness, Quartile skewness and Octile skewness. All have been devised to try to accommodate for the presence of outliers. However, those test statistics are demonstrably inefficient in the presence of outliers. That inefficiency motivates the approach in this paper; the development of an efficient and more robust skewness metric we call Split Sample Skewness, hereafter referred to as SSS. In this context, efficiency means that the SSS metric requires fewer sample observations than the less efficient metrics cited above to achieve the same level of statistical robustness. The name reflects a methodology that partitions the sample into two subgroups at the median. This paper displays the findings of multiple simulation studies adducing evidence bearing on the efficiency of split ample skewness relative to other measures of skewness. [ABSTRACT FROM AUTHOR]

Presence of one or more outliers in the observations affect inference procedure in statistical analysis. Designs robust against presence of a single outlier can be found in the literature for both regression and block design set-ups. In this paper, an attempt has been made to find a robust design in a block design set-up for the estimation of a full set of orthonormal treatment contrasts when there are more than one outlier among the observations. It appears that symmetry and balance play an important role in this study; it is seen that as we deviate from them, designs deviate from robustness. [ABSTRACT FROM AUTHOR]

Estimation and prediction of regression models with partition forms are widely applied techniques in statistical inference and data analysis. In this paper, we consider some fundamental inference problems regarding a linear random-effects model (LREM) and its reduced models without statistical distribution assumptions for error terms. We shall present a partition form of LREM and its correctly-reduced models, introduce the consistency concepts of the LREM and its reduced models, define the predictability/estimability of unknown parameters in the LREM and its reduced models, establish the matrix equations and analytical formulas associated with best linear unbiased predictors (BLUPs) and best linear unbiased estimators (BLUEs) of all unknown parameter vectors in the LREM and its reduced models, and present many fundamental decomposition equalities for the BLUPs/BLUEs of all unknown parameters in the LREM and its reduced models. [ABSTRACT FROM AUTHOR]

In many statistical analysis, a finite population may contain a large proportion of zero-and-one values that make the population distribution severely skew. Confidence intervals based on a normal approximation (NA) for such data may have low coverage probabilities. In this paper, we apply the methods of jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL) to discuss the confidence intervals for the mean of zero-and-one inflated population. Asymptotic distributions of the likelihood-type statistics are studied. Simulations are conducted to compare coverage probabilities with other methods under different distributions. Real data is given to illustrate the procedure of proposed methods. [ABSTRACT FROM AUTHOR]

The extropy measure proposed by Lad, Sanfilippo, and Agro (Statistical Science 30 (1):40–58, 2015) as a dual function of Shannon entropy has received a considerable attention in the last five years. In this paper, we first show a representation for the weighted extropy of ranked set sampling in terms of quantile and density-quantile functions. Then we provide some related results including monotone properties, stochastic orders, characterizations and sharp bounds. Moreover, the so obtained results show how the weighted extropy of ranked set sampling compares with its counterpart of simple random sampling. [ABSTRACT FROM AUTHOR]

This paper considers a new generalization of the negative binomial distribution arising from the Poisson-stopped sum of the Hurwitz–Lerch zeta distributions (Poisson-HLZD). The proposed generalization has much flexibility in shape (high probability at zero and long tail) as compared to other generalizations but retains computational tractability since, as a Poisson-stopped sum, it has a recurrence formula for its probabilities which facilitates its computation and applications. The flexibility of the Poisson-HLZD makes it a useful model for statistical analysis. The Poisson-HLZD is proved to have a mixed Poisson formulation and some of its probabilistic properties that are useful in determining the parameter space in estimation are derived. Its usefulness in data-fitting are compared with some well-known count frequency models. It is shown that the Poisson-HLZD fits very well for data with high zero counts or with very long tail. [ABSTRACT FROM AUTHOR]

This paper is concerned with high-dimensional asymptotic results for W- and Z- rules when the sample size N and the dimension are large. Firstly, we give a unified location and scale mixture expression of the standard normal distribution for W and Z statistics. Then, the EPMCs (Expected Probability of Misclassifications) of W- and Z- rules are obtained in expanded forms with errors of O (N − 2). It is pointed that Z-rule has smaller EER (Expected Error Rate) than W-rule when the prior probabilities are the same, neglecting the terms of O (N − 2). Further, asymptotic unbiased estimators are proposed for the EPMCs and the EERs of W- and Z- rules. Accuracies of our asymptotic results are checked numerically by conducting a Mote Carlo simulation. [ABSTRACT FROM AUTHOR]

In the present paper, we construct a new class of multivariate risk statistics, which are multivariate shortfall risk statistics. First, we propose the multivariate shortfall risk statistics. Then their basic properties are investigated, and the dual representation results are studied. Furthermore, their coherency is also characterized by means of the corresponding loss function. Finally, examples are given to illustrate the proposed multivariate shortfall risk statistics. [ABSTRACT FROM AUTHOR]

In this paper a bivariate gamma type distribution emanating from the diagonal elements of an inverse Wishart type distribution is developed; which in turn originates from the complex matrix variate elliptical class. From this, a bivariate Weibullised gamma type distribution is also presented, of which the bivariate Nakagami-m type is a special case. The derived results may be applied as decision statistics for a MIMO (multiple input multiple output) system with two transmit antennas. It is proposed that under this elliptical umbrella some performance measures such as the outage probability of MIMO systems can be analyzed in broad generality. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of constructing simultaneous confidence intervals for the cumulative distribution function of a normal distribution at several specified points. The procedure is based upon the observation of a random sample of independent observations from a normal distribution with an unknown mean and variance. A new methodology is proposed for obtaining confidence intervals with a specified overall simultaneous confidence level through the inversion of acceptance sets. Both one-sided and two-sided confidence intervals are considered. Some illustrations of the new method are provided, and comparisons are made with other approaches to the problem. [ABSTRACT FROM PUBLISHER]

This paper focuses on the corrections to the score test statistic under the exponential family nonlinear model. We use Monte Carlo simulations to compare the corrected statistics and their uncorrected versions and to examine the impact of the number of nuisance parameters on their finite-sample behaviors for the normal nonlinear regression model. The numerical results have shown that the corrected score statistic performs better than the uncorrected version. Finally, we perform a statistical analysis with real data by using the approach proposed in the article. [ABSTRACT FROM AUTHOR]

In this paper we generalize the exponential family (EF) of distributions into a wider family which includes important distributions such as the normal, log-normal, Student-t, Cauchy, logistic and Birnbaum-Saunders distributions. Furthermore, we derive several characteristics of the proposed family. The importance of such family is also discussed. [ABSTRACT FROM AUTHOR]

The ratio of normal tail probabilities and the ratio of Student’sttail probabilities have gained an increased attention in statistics and related areas. However, they are not well studied in the literature. In this paper, we systematically study the functional behaviors of these two ratios. Meanwhile, we explore their difference as well as their relationship. It is surprising that the two ratios behave very different to each other. Finally, we conclude the paper by conducting some lower and upper bounds for the two ratios. [ABSTRACT FROM PUBLISHER]

In case of sensitive research, estimation of mean is a major concern in survey studies and regression estimators utilizing traditional regression coefficient are the most favored choices for it. Recently, Zaman and Bulut [2018. Modified ratio estimators using robust regression methods. Communications in Statistics - Theory and Methods, DOI: 10.1080/03610926.2018.1441419] have developed a class of ratio-type estimators for the mean estimation of non-sensitive variable utilizing robust regression coefficients. In this paper, we have generalized their family of estimators to the case where the study variable refers to sensitive issues which produces measurement errors due to non-responses and/or untruthful reporting. These errors may be reduced by enhancing respondent cooperation through scrambled response methods that mask the true value of the sensitive variable. Hence, two scrambled response models Pollock and Bek (1976), Bar-Lev, Bobovitch, and Boukai (2004) are discussed for the purposes of this article. We have also developed a class of robust-regression type estimators in case of sensitive research. Some estimators belonging to the class are shown and the mean square errors are determined. Theoretical and empirical illustration is done through real and artificial data sets for assessing the performance of adapted and proposed class. [ABSTRACT FROM AUTHOR]

In this paper, the complete convergence and the complete moment convergence for maximal randomly weighted sums of widely orthant-dependent (WOD, in short) random variables are investigated. By using the main results, we further consider three striking and useful applications in probability and statistics. Our results generalize the corresponding earlier ones. [ABSTRACT FROM AUTHOR]

In the literature of information theory, the concept of generalized entropy has been proposed and the length-based shift dependent information measure has been studied. In this paper, the concept of weighted generalized entropy has been introduced. The properties of weighted generalized residual entropy and weighted generalized past entropy are also discussed. [ABSTRACT FROM AUTHOR]

In practice, it is often not possible to find an appropriate family of distributions which can be used for fitting the sample distribution with high precision. In these cases, it seems to be opportune to search for the best approximation by a family of distributions instead of an exact fit. In this paper, we consider the Anderson–Darling statistic with plugged-in minimum distance estimator for the parameter vector. We prove asymptotic normality of the Anderson–Darling statistic which is used for a test of goodness of approximation. Moreover, we introduce a measure of discrepancy between the sample distribution and the model class. [ABSTRACT FROM AUTHOR]

This paper investigates the statistical analysis of grouped accelerated temperature cycling test data when the product lifetime follows a Weibull distribution. A log-linear acceleration equation is derived from the Coffin-Manson model. The problem is transformed to a constant-stress accelerated life test with grouped data and multiple acceleration variables. The Jeffreys prior and reference priors are derived. Maximum likelihood estimation and Bayesian estimation with objective priors are obtained by applying the technique of data augmentation. A simulation study shows that both of these two methods perform well when sample size is large, and the Bayesian method gives better performance under small sample sizes. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the regression of a surrogated scalar response variable given a functional random one. We construct an estimator of the regression operator by using, in addition to the available (true) response data, a surrogate data. We then establish some asymptotic properties of the constructed estimator in terms of the almost-complete and the quadratic mean convergences. Notice that the obtained results generalize a part of the results obtained in the finite dimensional framework. Finally, an illustration on the applicability of our results on both simulated data and a real dataset was realized. We have thus shown the superiority of our estimator on classical estimators when we are lacking complete data. [ABSTRACT FROM AUTHOR]

This paper considers a new model in search theory to find a randomly located target in the 3-dimensional space. An approximation algorithm that facilitates searching procedures for searchers or robots is presented. The expected time to detect the target is also proved. The statistical analysis by calculating the optimal search strategy which minimizes the time to detect the target, assuming trivariate standard normal distribution is provided, and the technique by flowcharts is designed as well. The effectiveness of this strategy is illustrated by introducing an application from real world. [ABSTRACT FROM AUTHOR]

This paper considers the statistical analysis of masked data in a parallel system with inverse Weibull distributed components under type II censoring. Based on Gamma conjugate prior, the Bayesian estimation as well as the hierarchical Bayesian estimation for the parameters and the reliability function of system are obtained by using the Bayesian theory and the hierarchical Bayesian method. Finally, Monte Carlo simulations are provided to compare the performances of the estimates under different masking probabilities and effective sample sizes. [ABSTRACT FROM AUTHOR]

Parametric regression models with certain parameter restrictions occur widely in statistical data analysis and inference. In some cases, we may wish to impose linear constraints on some subsets of fixed parameters in a given parametric regression model. This paper is concerned with some fundamental inference problems on a general linear model y = X β + ε in which the unknown parameter vector β is subject to a linear matrix equation restriction A β = b . We shall introduce the technical concepts and definitions of consistency, predictability, estimability, and formulas of the best linear unbiased predictors/best linear unbiased estimators (BLUPs/BLUEs) under a constrained general linear model (CGLM). We then show how to establish BLUPs/BLUEs of all unknown parameters in the CGLM, and present various properties of the BLUPs/BLUEs using the methodology of matrix ranks and inertias. [ABSTRACT FROM AUTHOR]

Variable selection using regularization approaches is an essential part of any statistical analysis and yet has been somewhat neglected for the semiparametric random-effects conditional density (RECD) models with longitudinal data. In this paper, we show how the regularization approach for variable selection can be adapted to the RECD models with longitudinal data. The computational and theoretical properties for variable selection consistency are established. Comprehensive simulation studies and a real data analysis further demonstrate the merits of our approach. [ABSTRACT FROM AUTHOR]

This paper presents a new method to estimate the quantiles of generic statistics by combining the concept of random weighting with importance resampling. This method converts the problem of quantile estimation to a dual problem of tail probabilities estimation. Random weighting theories are established to calculate the optimal resampling weights for estimation of tail probabilities via sequential variance minimization. Subsequently, the quantile estimation is constructed by using the obtained optimal resampling weights. Experimental results on real and simulated data sets demonstrate that the proposed random weighting method can effectively estimate the quantiles of generic statistics. [ABSTRACT FROM AUTHOR]

Single set deletion influence analysis was introduced by Cook and Weisburg (Cook, R. D., Weisberg, S. (1982). Residuals and Influence in Regression. Chapman and Hall: New York); Pena and Yohai (Pena, D., Yohai, V. J. (1995). The detection of influential subsets in linear regression by using an influence matrix. J. R. Statist. Soc. B 57(1): 145–156); Barrett and Ling (Barrett, B. E., Ling, R. F. (1992). General classes of influence measures for multivariate regression. J. Amer. Stiatist. Assoc. 87(417):184–191); and Cook (Cook, R. D. (2000). Detection of influential observations inlinear regression. Technometrics 42(1):65–68). Multiple sets of deletion measures and conditional influence analysis are given in regression models by Wang and Critchley (Wang, D. Q., Critchley, F. (2000). Multiple deletion measures and influence in regression model. Communication in Statistics: Theory and Methods 29(11): 2391–2404). The present paper is concerned with multiple sets of deletion measures and masking problems in linear regression models. Masking problems have been discussed by Atkinson (Atkinson, A. C. (1985). Plot, Transformations and Regression. Clarendon: Oxford) and Lawrance (Lawrance, A. J. (1995). Deletion influence and masking in regression. J. R. Statist. Soc. B 57(1):181–189). In this paper we are particularly interested in conditional influence and masking problems for the detection of two influential subsets. [ABSTRACT FROM AUTHOR]

The paper considers the development of inferential techniques for the general half-normal distribution based on the maximum likelihood method. Point estimates and large-sample confidence sets are derived for the distribution's parameters, and the use of the confidence sets as a basis for hypothesis testing is discussed. Data on the percentage body fat of elite male athletes are used to illustrate the application of the new methods. [ABSTRACT FROM AUTHOR]

Score method in hypothesis testing is one of Professor C. R. Rao's great contributions to statistics. It provides a simple and unified way to test some simple and composite hypotheses in many statistical problems. Some popular tests in statistical practice derived with the help of intuitions can be shown as score tests under some statistical models. The subject-years test and log-rank test in survival analysis are two of the examples. In this paper, we first introduce these two examples. After formulating these two tests as score tests, we then review some recent results on the Bartlett type adjustments for these tests. [ABSTRACT FROM AUTHOR]

In Bayesian theory, calculating a posterior probability distribution is highly important but typically difficult. Therefore, some methods have been proposed to deal with such problem, among which, the most popular one is the asymptotic expansions of posterior distributions. In this paper, we propose an alternative approach, named a random weighting method, for scaled posterior distributions, and give an ideal convergence rate,o(n( − 1/2)), which serves as the theoretical guarantee for methods of numerical simulations. [ABSTRACT FROM AUTHOR]

We discuss the use of variograms for covariance modeling under the Kriging model to assess tolerances on manufactured parts. The variogram is very informative about the spatial dependence and it is favored by researchers in the choice of a correlation function. It may give evidence of anisotropy and of nugget effect too. In this paper, various variogram estimators from literature are used for comparing and evaluating their properties in assessing the surface error of three workpieces manufactured with different machining precisions. Contrary to the common engineering belief, the variograms give evidence of both technological signatures and systematic errors of the measurement machines. [ABSTRACT FROM PUBLISHER]

In this paper, a new censoring scheme named by adaptive progressively interval censoring scheme is introduced. The competing risks data come from Marshall–Olkin extended Chen distribution under the new censoring scheme with random removals. We obtain the maximum likelihood estimators of the unknown parameters and the reliability function by using the EM algorithm based on the failure data. In addition, the bootstrap percentile confidence intervals and bootstrap-t confidence intervals of the unknown parameters are obtained. To test the equality of the competing risks model, the likelihood ratio tests are performed. Then, Monte Carlo simulation is conducted to evaluate the performance of the estimators under different sample sizes and removal schemes. Finally, a real data set is analyzed for illustration purpose. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two new classes of risk statistics, named convex and positively homogeneous systemic risk statistics, respectively. Structural decomposition results and representation results for them are provided. These new risk statistics can be considered as a kind of systemic risk extension of risk statistics introduced by Kou, Peng, and Heyde, and also empirical versions of system risk measures introduced by Cehn, Iyengar, and Mollemi and Kromer, Overbeck, and Zich. Finally, some examples are also given. [ABSTRACT FROM AUTHOR]

This paper extends the one-way heteroskedasticity score test of Holly and Gardiol (2000, In: Krishnakumar, J, Ronchetti, E (Eds.), Panel Data Econometrics: Future Directions, North-Holland, Amsterdam, pp. 199–211) to two conditional Lagrange Multiplier (LM) tests of heteroskedasticity under contiguous alternatives within the two-way error components model framework. In each case, the derivation of Rao's efficient score statistics for testing heteroskedasticity is first obtained. Then, based on a specific set of assumptions, the asymptotic distribution of the score under contiguous alternatives is established. Finally, the expression for the score test statistic in the presence of heteroskedasticity and related asymptotic local powers of these score test statistics are derived and discussed. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to propose a number of semiautomatic criteria for the joint selection of the model and the information set in the heteroskedastic context considered in Lejeune [Lejeune, B. (1997). Second order pseudo-maxim urn likelihood estimation and conditional variance misspecification. University of Liege. ERUDITE and CORE]. Particular cases of this methodology are the posterior information criterion (PIC) and the posterior information criterion for a forecasts (PICF) of Phillips [Phillips. P. C. B. (1996). Econometric model determination. Econometrica 64:763-812] and Phillips and Ploberger [Phillips. P. C. B., Ploberger. W. (1994). Posterior odds testing for a unit root with data-based model setection. Econometric Theory 10:774-808] and the Bayesian information criterion (BIC) criterion of Schwarz [Schwarz. G. (1978). Estimating the dimension of a model. Ann, Statist, 6:461-464]. Sufficient conditions for the weak and strong consistencies of the proposed criteria are provided. Furthermore, the behaviors of these criteria are analyzed in the presence of sinieturat changes. [ABSTRACT FROM AUTHOR]

Latent regression models are powerful tools for studying the relation between a latent outcome variable and observed covariates. In this paper goodness of fit tests for latent regression models based on loglinear Rasch models are proposed. The properties of the tests are discussed using a simulation. [ABSTRACT FROM AUTHOR]

For an orthogonally blocked experiment, Khuri [Khuri, A. I. (1992). Response surface models with random block effects. Technometrics 34:26-37] has shown that the ordinary least squares estimator, the generalized least squares estimator and the intra-block estimator of the factor effects in a response surface model with random block effects coincide. The ordinary least squares estimator ignores the blocks, whereas the generalized least squares and the intra-block estimators treat the block effects as random and fixed, respectively. As shown in this paper, the equivalence does not hold for the estimation of the intercept when the block sizes are heterogeneous. Practical examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Use of the MVUE for the inverse-Gaussian distribution has been recently proposed by Nguyen and Dinh [Nguyen, T. T., Dinh, K. T. (2003). Exact EDF goodnes-of-fit tests for inverse Gaussian distributions. Comm. Statist. (Simulation and Computation) 32(2):505-516] where a sequential application based on Rosenblatt's transformation [Rosenblatt, M. (1952). Remarks on a multivariate transformation. Ann. Math. Statist. 23:470-472] led the authors to solve the composite goodness-of-fit problem by solving the surrogate simple goodness-of-fit problem, of testing uniformity of the independent transformed variables. In this note, we observe first that the proposal is not new since it was proposed in a rather general setting in O'Reilly and Quesenberry [O'Reilly, F., Quesenberry, C. P. (1973). The conditional probability integral transformation and applications to obtain composite chi-square goodness-of-fit tests. Ann. Statist. 1:74-83]. It is shown on the other hand that the results in the paper of Nguyen and Dinh (2003) are incorrect in their Sec. 4, specially the Monte Carlo figures reported. Power simulations are provided here comparing these corrected results with two previously reported goodness-of-fit tests for the inverse-Gaussian; the modified Kolmogorov--Smirnov test in Edgeman et al. [Edgeman, R. L., Scott, R. C., Pavur, R. J. (1988). A modified Kolmogorov-Smirnov test for inverse Gaussian distribution with unknown parameters. Comm. Statist. 17(B): 1203-1212] and the A² based method in O'Reilly and Rueda [O'Reilly, F., Rueda, R. (1992). Goodness of fit for the inverse Gaussian distribution. T Can. J. Statist. 20(4):387-397]. The results show clearly that there is a large loss of power in the method explored in Nguyen and Dinh (2003) due to an implicit exogenous randomization. [ABSTRACT FROM AUTHOR]

This paper derives the asymptotic distributions of the estimators of the unified process capability indices Cp(u, v) and Cpa(u, v) for arbitrary population under general, regularity conditions, assuming that the fourth moment about the mean exists. [ABSTRACT FROM AUTHOR]

In this paper, we extend to our knowledge, for the first case, some reliability results using homogeneous semi-Markov processes to the case of homogeneous modeling semi-Markov processes. Moreover, we apply some of our preceding results to give the numerical solutions and so the possibility to treat real life problems for which non-homogeneity in time is important. [ABSTRACT FROM AUTHOR]

In this paper, the Bayesian robustness of the posterior predictive p-value is studied. First of all, it is proved that Lavine's linearization technique can be extended for analyzing this problem. Then, the result is applied to the ε-contamination class of prior distributions. [ABSTRACT FROM AUTHOR]

The paper present an explicit expression for the density of a n-dimensional random vector with a singular Elliptical distribution. Based on this, the densities of the generalized Chi-squared and generalized t distributions are derived, examining the Pearson Type VII distribution and Kotz Type distribution (as specific Elliptical distributions). Finally, the results are applied to the study of the distribution of the residuals of an Elliptical linear model and the distribution of the t-statistic, based on a sample from an Elliptical population. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new measure for the analysis of association in cross-classifications having ordered categories. Association is measured in terms of the odd-ratios in 2 × 2 subtables formed from adjacent rows and adjacent columns. We focus our attention in the uniform association model. Our measure is based in the family of divergences introduced by Burbea and Rao []. Some well-known sets of data are reanalyzed and a simulation study is presented to analyze the behavior of the new families of test statistics introduced in this paper. [ABSTRACT FROM AUTHOR]

Our paper provides sample size formulas for multiple comparisons of k treatments with a control as well as with pairwise comparisons of k treatments when observing proportions. The test statistics we regard use the arcsin transformation of the proportions. We determine sample sizes with preassigned all-pairs, any-pair and per-pair power considering the control either of the comparisonwise or of the familywise type I error rate. [ABSTRACT FROM AUTHOR]

In this paper we introduce a class of estimators which includes the ordinary least squares (OLS), the principal components regression (PCR) and the Liu estimator (). In particular, we show that our new estimator is superior, in the scalar mean-squared error (mse) sense, to the Liu estimator, to the OLS estimator and to the PCR estimator. [ABSTRACT FROM AUTHOR]

Since Rao introduced the Quadratic Entropy (QE) in 1982, results on mathematical and statistical properties of the QE and its applications in data analysis and population indices have been published in the literature. In this paper, we study the asymptotic efficiency of the analysis of Rao's quadratic entropy (ANOQE) which is a generalization of the classical analysis of variance (ANOVA). Based on the results of Liu and Rao (1) and Liu (2) on asymptotic distribution and the bootstrap of the ANOQE, we derive the Bahadur's asymptotic efficiency of the ANOQE and compare efficiency of ANOQE tests based on different QE's. [ABSTRACT FROM AUTHOR]

Estimating parameters of a two dimensional frequency model is an important problem in statistical signal processing. In this paper, we consider the two-dimensional frequency model in presence of an additive stationary noise. We consider two different estimators and obtain their asymptotic properties. The asymptotic properties can be used to construct confidence intervals of the unknown parameters and for testing purposes also. The small sample performances of these estimators are observed using numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a procedure based on the delete-1 cross-validation is given for estimating the number of superimposed exponential signals, its limiting behavior is explored and it is shown that the probability of overestimating the true number of signals is greater than a positive constant for sufficiently large samples. Also a general procedure based on the crossvalidation is presented when the deletion proceeds according to a collection of subsets of indices. The result is similar to the delete-1 cross-validation if the number of deletions is fixed. The simulation results are provided for the performance of the procedure when the collections of subsets of indices are chosen as those suggested by Shao (1) in a linear model selection problem. [ABSTRACT FROM AUTHOR]

A great deal of research has focused on improving the bias properties of kernel estimators. One proposal involves removing the restriction of non-negativity on the kernel to construct "higher-order" kernels that eliminate additional terms in the Taylor's series expansion of the bias. This paper considers an alternative that uses a local approach to bandwidth selection to not only reduce the bias, but to eliminate it entirely. These so-called "zero-bias bandwidths" are shown to exist for univariate and multivariate kernel density estimation as well as kernel regression. Implications of the existence of such bandwidths are discussed. An estimation strategy is presented, and the extent of the reduction or elimination of bias in practice is studied through simulation and example. A great deal of research has focused on improving the bias properties of kernel estimators. One proposal involves removing the restriction of non-negativity on the kernel to construct "higher-order" kernels that eliminate additional terms in the Taylor's series expansion of the bias. This paper considers an alternative that uses a local approach to bandwidth selection to not only reduce the bias, but to eliminate it entirely. These so-called "zero-bias bandwidths" are shown to exist for univariate and multivariate kernel density estimation as well as kernel regression. Implications of the existence of such bandwidths are discussed. An estimation strategy is presented, and the extent of the reduction or elimination of bias in practice is studied through simulation and example. [ABSTRACT FROM AUTHOR]