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 Metrika   [SJR: 0.605]   [H-I: 30]   [4 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1435-926X - ISSN (Online) 0026-1335    Published by Springer-Verlag  [2352 journals]
• Nonparametric estimation for self-selected interval data collected through
a two-stage approach
• Authors: Angel G. Angelov; Magnus Ekström
Pages: 377 - 399
Abstract: Abstract Self-selected interval data arise in questionnaire surveys when respondents are free to answer with any interval without having pre-specified ranges. This type of data is a special case of interval-censored data in which the assumption of noninformative censoring is violated, and thus the standard methods for interval-censored data (e.g. Turnbull’s estimator) are not appropriate because they can produce biased results. Based on a certain sampling scheme, this paper suggests a nonparametric maximum likelihood estimator of the underlying distribution function. The consistency of the estimator is proven under general assumptions, and an iterative procedure for finding the estimate is proposed. The performance of the method is investigated in a simulation study.
PubDate: 2017-05-01
DOI: 10.1007/s00184-017-0610-7
Issue No: Vol. 80, No. 4 (2017)

• Efficient paired choice designs with fewer choice pairs
• Authors: Aloke Dey; Rakhi Singh; Ashish Das
Pages: 309 - 317
Abstract: Abstract For paired choice experiments, two new construction methods of designs are proposed for the estimation of the main effects. In many cases, these designs require about 30–50% fewer choice pairs than the existing designs and at the same time have reasonably high D-efficiencies for the estimation of the main effects. Furthermore, as against the existing efficient designs, our designs have higher D-efficiencies for the same number of choice pairs.
PubDate: 2017-04-01
DOI: 10.1007/s00184-016-0605-9
Issue No: Vol. 80, No. 3 (2017)

• Exact inference for the difference of Laplace location parameters
• Authors: Maria Tafiadi; George Iliopoulos
Abstract: Abstract We consider exact procedures for testing the equality of means (location parameters) of two Laplace populations with equal scale parameters based on corresponding independent random samples. The test statistics are based on either the maximum likelihood estimators or the best linear unbiased estimators of the Laplace parameters. By conditioning on certain quantities we manage to express their exact distributions as mixtures of ratios of linear combinations of standard exponential random variables. This allows us to find their exact quantiles and tabulate them for several sample sizes. The powers of the tests are compared either numerically or by simulation. Exact confidence intervals for the difference of the means corresponding to those tests are also constructed. The exact procedures are illustrated via a real data example.
PubDate: 2017-10-10
DOI: 10.1007/s00184-017-0630-3

• Adjusted Pearson Chi-Square feature screening for multi-classification
with ultrahigh dimensional data
• Authors: Lyu Ni; Fang Fang; Fangjiao Wan
Abstract: Abstract Huang et al. (J Bus Econ Stat 32:237–244, 2014) first proposed a Pearson Chi-Square based feature screening procedure tailored to multi-classification problem with ultrahigh dimensional categorical covariates, which is a common problem in practice but has seldom been discussed in the literature. However, their work establishes the sure screening property only in a limited setting. Moreover, the p value based adjustments when the number of categories involved by each covariate is different do not work well in several practical situations. In this paper, we propose an adjusted Pearson Chi-Square feature screening procedure and a modified method for tuning parameter selection. Theoretically, we establish the sure screening property of the proposed method in general settings. Empirically, the proposed method is more successful than Pearson Chi-Square feature screening in handling non-equal numbers of covariate categories in finite samples. Results of three simulation studies and one real data analysis are presented. Our work together with Huang et al. (J Bus Econ Stat 32:237–244, 2014) establishes a solid theoretical foundation and empirical evidence for the family of Pearson Chi-Square based feature screening methods.
PubDate: 2017-10-07
DOI: 10.1007/s00184-017-0629-9

• Stochastic comparisons of order statistics from heterogeneous random
variables with Archimedean copula
• Authors: M. Mesfioui; M. Kayid; S. Izadkhah
Abstract: Abstract This article is devoted to characterize several ordering properties of the maximum order statistic of heterogenous random variables with an Archimedean copula. Some examples are also included to illustrate the obtained results.
PubDate: 2017-10-03
DOI: 10.1007/s00184-017-0626-z

• Multivariate saddlepoint tests on the mean direction of the von
Mises–Fisher distribution
• Authors: R. Gatto
Abstract: Abstract This article provides P values for two new tests on the mean direction of the von Mises–Fisher distribution. The test statistics are obtained from the exponent of the saddlepoint approximation to the density of M-estimators, as suggested by Robinson et al. (Ann Stat 31:1154–1169, 2003). These test statistics are chi-square distributed with asymptotically small relative errors. Despite the high dimensionality of the problem, the proposed P values are accurate and simple to compute. The numerical precision of the P values of the new tests is illustrated by some simulation studies.
PubDate: 2017-09-13
DOI: 10.1007/s00184-017-0625-0

• R-optimal designs for multi-factor models with heteroscedastic errors
• Authors: Lei He; Rong-Xian Yue
Abstract: Abstract In this paper, we consider the R-optimal design problem for multi-factor regression models with heteroscedastic errors. It is shown that a R-optimal design for the heteroscedastic Kronecker product model is given by the product of the R-optimal designs for the marginal one-factor models. However, R-optimal designs for the additive models can be constructed from R-optimal designs for the one-factor models only if sufficient conditions are satisfied. Several examples are presented to illustrate and check optimal designs based on R-optimality criterion.
PubDate: 2017-08-02
DOI: 10.1007/s00184-017-0624-1

• Estimating moments in ANOVA-type mixed models
• Authors: Zaixing Li; Fei Chen; Lixing Zhu
Abstract: Abstract In the paper, a simple projection-based method is systematically developed to estimate the qth ( $$q\ge 2$$ ) order moments of random effects and errors in the ANOVA type mixed model (ANOVAMM), where the response may not be divided into independent sub-vectors. All the estimates are weakly consistent and the second-order moment estimates are strongly consistent. Besides, the derived estimates are different from those in mixed models with cluster design. Simulation studies are conducted to examine the finite sample performance of the estimates and two real data examples are analyzed for illustration.
PubDate: 2017-08-02
DOI: 10.1007/s00184-017-0623-2

• Some general points on the $$I^2$$ I 2 -measure of heterogeneity in
meta-analysis
• Authors: Dankmar Böhning; Rattana Lerdsuwansri; Heinz Holling
Abstract: Abstract Meta-analysis has developed to be a most important tool in evaluation research. Heterogeneity is an issue that is present in almost any meta-analysis. However, the magnitude of heterogeneity differs across meta-analyses. In this respect, Higgins’ $$I^2$$ has emerged to be one of the most used and, potentially, one of the most useful measures as it provides quantification of the amount of heterogeneity involved in a given meta-analysis. Higgins’ $$I^2$$ is conventionally interpreted, in the sense of a variance component analysis, as the proportion of total variance due to heterogeneity. However, this interpretation is not entirely justified as the second part involved in defining the total variation, usually denoted as $$s^2$$ , is not an average of the study-specific variances, but in fact some other function of the study-specific variances. We show that $$s^2$$ is asymptotically identical to the harmonic mean of the study-specific variances and, for any number of studies, is at least as large as the harmonic mean with the inequality being sharp if all study-specific variances agree. This justifies, from our point of view, the interpretation of explained variance, at least for meta-analyses with larger number of component studies or small variation in study-specific variances. These points are illustrated by a number of empirical meta-analyses as well as simulation work.
PubDate: 2017-07-22
DOI: 10.1007/s00184-017-0622-3

• Multidimensional strong large deviation results
• Authors: Cyrille Joutard
Abstract: Abstract We establish strong large deviation results for an arbitrary sequence of random vectors under some assumptions on the normalized cumulant generating function. In other words, we give asymptotic approximations for a multivariate tail probability of the same kind as the one obtained by Bahadur and Rao (Ann Math Stat 31:1015–1027, 1960) for the sample mean (in the one-dimensional case). The proof of our results follows the same lines as in Chaganty and Sethuraman (J Stat Plan Inference, 55:265–280, 1996). We also present three statistical applications to illustrate our results, the first one dealing with a vector of independent sample variances, the second one with a Gaussian multiple linear regression model and the third one with the multivariate Nadaraya–Watson estimator. Some numerical results are also presented for the first two applications.
PubDate: 2017-07-15
DOI: 10.1007/s00184-017-0621-4

• A new measure of association between random variables
Abstract: Abstract We propose a new measure of association between two continuous random variables X and Y based on the covariance between X and the log-odds rate associated to Y. The proposed index of correlation lies in the range [ $$-1$$ , 1]. We show that the extremes of the range, i.e., $$-1$$ and 1, are attainable by the Fr $$\acute{\mathrm{e}}$$ chet bivariate minimal and maximal distributions, respectively. It is also shown that if X and Y have bivariate normal distribution, the resulting measure of correlation equals the Pearson correlation coefficient $$\rho$$ . Some interpretations and relationships to other variability measures are presented. Among others, it is shown that for non-negative random variables the proposed association measure can be represented in terms of the mean residual and mean inactivity functions. Some illustrative examples are also provided.
PubDate: 2017-07-03
DOI: 10.1007/s00184-017-0620-5

• Stochastic comparisons of distorted distributions, coherent systems and
mixtures with ordered components
• Authors: Jorge Navarro; Yolanda del Águila
Abstract: Abstract A distribution function F is a generalized distorted distribution of the distribution functions $$F_1,\ldots ,F_n$$ if $$F=Q(F_1,\ldots ,F_n)$$ for an increasing continuous distortion function Q such that $$Q(0,\ldots ,0)=0$$ and $$Q(1,\ldots ,1)=1$$ . In this paper, necessary and sufficient conditions for the stochastic (ST) and the hazard rate (HR) orderings of generalized distorted distributions are provided when the distributions $$F_1,\ldots ,F_n$$ are ordered. These results are used to obtain distribution-free ordering properties for coherent systems with heterogeneous components. In particular, we determine all the ST and HR orderings for coherent systems with 1–3 independent components. We also compare systems with dependent components. The results on distorted distributions are also used to get comparisons of finite mixtures.
PubDate: 2017-06-28
DOI: 10.1007/s00184-017-0619-y

• Weak and strong laws of large numbers for arrays of rowwise END random
variables and their applications
• Authors: Aiting Shen; Andrei Volodin
Abstract: Abstract In the paper, the Marcinkiewicz–Zygmund type moment inequality for extended negatively dependent (END, in short) random variables is established. Under some suitable conditions of uniform integrability, the $$L_r$$ convergence, weak law of large numbers and strong law of large numbers for usual normed sums and weighted sums of arrays of rowwise END random variables are investigated by using the Marcinkiewicz–Zygmund type moment inequality. In addition, some applications of the $$L_r$$ convergence, weak and strong laws of large numbers to nonparametric regression models based on END errors are provided. The results obtained in the paper generalize or improve some corresponding ones for negatively associated random variables and negatively orthant dependent random variables.
PubDate: 2017-05-22
DOI: 10.1007/s00184-017-0618-z

• Testing the compounding structure of the CP-INARCH model
• Authors: Christian H. Weiß; Esmeralda Gonçalves; Nazaré Mendes Lopes
Abstract: Abstract A statistical test to distinguish between a Poisson INARCH model and a Compound Poisson INARCH model is proposed, based on the form of the probability generating function of the compounding distribution of the conditional law of the model. For first-order autoregression, the normality of the test statistics’ asymptotic distribution is established, either in the case where the model parameters are specified, or when such parameters are consistently estimated. As the test statistics’ law involves the moments of inverse conditional means of the Compound Poisson INARCH process, the analysis of their existence and calculation is performed by two approaches. For higher-order autoregressions, we use a bootstrap implementation of the test. A simulation study illustrating the finite-sample performance of this test methodology in what concerns its size and power concludes the paper.
PubDate: 2017-05-03
DOI: 10.1007/s00184-017-0617-0

• Focused information criterion and model averaging in censored quantile
regression
• Authors: Jiang Du; Zhongzhan Zhang; Tianfa Xie
Abstract: Abstract In this paper, we study model selection and model averaging for quantile regression with randomly right censored response. We consider a semi-parametric censored quantile regression model without distribution assumptions. Under general conditions, a focused information criterion and a frequentist model averaging estimator are proposed, and theoretical properties of the proposed methods are established. The performances of the procedures are illustrated by extensive simulations and the primary biliary cirrhosis data.
PubDate: 2017-04-29
DOI: 10.1007/s00184-017-0616-1

• Estimation of the order restricted scale parameters for two populations
from the Lomax distribution
• Authors: Constantinos Petropoulos
Abstract: Abstract The usual methods of estimating the unknown parameters of a distribution, use only the information given from the sample data. In many cases, there is, also, another important information for estimating the unknown parameters of our model, such as the order of these parameters, and this last information improves the quality of estimation. In this paper, we deal with the problem of estimating the ordered scale parameters from two populations of the multivariate Lomax distribution, with unknown location parameters. It is proved that the best equivariant estimators of the scale parameters (in the unrestricted case) are not admissible and we construct estimators that improve upon the usual ones (when these parameters are known to be ordered).
PubDate: 2017-03-16
DOI: 10.1007/s00184-017-0615-2

• Minimum distance estimators for count data based on the probability
generating function with applications
• Authors: M. D. Jiménez-Gamero; A. Batsidis
Abstract: Abstract This paper studies properties of parameter estimators obtained by minimizing a distance between the empirical probability generating function and the probability generating function of a model for count data. Specifically, it is shown that, under certain not restrictive conditions, the resulting estimators are consistent and, suitably normalized, asymptotically normal. These properties hold even if the model is misspecified. Three applications of the obtained results are considered. First, we revisit the goodness-of-fit problem for count data and propose a weighted bootstrap estimator of the null distribution of test statistics based on the above cited distance. Second, we give a probability generating function version of the model selection test problem for separate, overlapping and nested families of distributions. Finally, we provide an application to the problem of testing for separate families of distributions. All applications are illustrated with numerical examples.
PubDate: 2017-03-15
DOI: 10.1007/s00184-017-0614-3

• On bending (down and up) property of reliability measures in mixtures
• Authors: F. G. Badía; Ji Hwan Cha
Abstract: Abstract In this paper, we study the bending property of the failure rate, reversed hazard rate, mean residual life and mean inactivity time in mixtures. For those four reliability measures, the weak and strong bending properties are studied and discussed, respectively. The results are illustrated with suitable examples, where most of them are relative to the model of proportional reliability measures.
PubDate: 2017-03-14
DOI: 10.1007/s00184-017-0613-4

• Estimation in generalized bivariate Birnbaum–Saunders models
• Authors: Helton Saulo; N. Balakrishnan; Xiaojun Zhu; Jhon F. B. Gonzales; Jeremias Leão
Abstract: Abstract In this paper, we propose two moment-type estimation methods for the parameters of the generalized bivariate Birnbaum–Saunders distribution by taking advantage of some properties of the distribution. The proposed moment-type estimators are easy to compute and always exist uniquely. We derive the asymptotic distributions of these estimators and carry out a simulation study to evaluate the performance of all these estimators. The probability coverages of confidence intervals are also discussed. Finally, two examples are used to illustrate the proposed methods.
PubDate: 2017-03-10
DOI: 10.1007/s00184-017-0612-5

• On generalized progressive hybrid censoring in presence of competing risks
• Authors: Arnab Koley; Debasis Kundu
Abstract: Abstract The progressive Type-II hybrid censoring scheme introduced by Kundu and Joarder (Comput Stat Data Anal 50:2509–2528, 2006), has received some attention in the last few years. One major drawback of this censoring scheme is that very few observations (even no observation at all) may be observed at the end of the experiment. To overcome this problem, Cho et al. (Stat Methodol 23:18–34, 2015) recently introduced generalized progressive censoring which ensures to get a pre specified number of failures. In this paper we analyze generalized progressive censored data in presence of competing risks. For brevity we have considered only two competing causes of failures, and it is assumed that the lifetime of the competing causes follow one parameter exponential distributions with different scale parameters. We obtain the maximum likelihood estimators of the unknown parameters and also provide their exact distributions. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals can be obtained. Asymptotic and bootstrap confidence intervals are also provided for comparison purposes. We further consider the Bayesian analysis of the unknown parameters under a very flexible beta–gamma prior. We provide the Bayes estimates and the associated credible intervals of the unknown parameters based on the above priors. We present extensive simulation results to see the effectiveness of the proposed method and finally one real data set is analyzed for illustrative purpose.
PubDate: 2017-02-24
DOI: 10.1007/s00184-017-0611-6

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