for Journals by Title or ISSN for Articles by Keywords help

Publisher: Springer-Verlag (Total: 2350 journals)

 Annals of the Institute of Statistical Mathematics   [SJR: 0.931]   [H-I: 31]   [1 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1572-9052 - ISSN (Online) 0020-3157    Published by Springer-Verlag  [2350 journals]
• Robust variable selection for finite mixture regression models
• Authors: Qingguo Tang; R. J. Karunamuni
Pages: 489 - 521
Abstract: Finite mixture regression (FMR) models are frequently used in statistical modeling, often with many covariates with low significance. Variable selection techniques can be employed to identify the covariates with little influence on the response. The problem of variable selection in FMR models is studied here. Penalized likelihood-based approaches are sensitive to data contamination, and their efficiency may be significantly reduced when the model is slightly misspecified. We propose a new robust variable selection procedure for FMR models. The proposed method is based on minimum-distance techniques, which seem to have some automatic robustness to model misspecification. We show that the proposed estimator has the variable selection consistency and oracle property. The finite-sample breakdown point of the estimator is established to demonstrate its robustness. We examine small-sample and robustness properties of the estimator using a Monte Carlo study. We also analyze a real data set.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0602-4
Issue No: Vol. 70, No. 3 (2018)

• Versatile estimation in censored single-index hazards regression
• Authors: Chin-Tsang Chiang; Shao-Hsuan Wang; Ming-Yueh Huang
Pages: 523 - 551
Abstract: One attractive advantage of the presented single-index hazards regression is that it can take into account possibly time-dependent covariates. In such a model formulation, the main theme of this research is to develop a theoretically valid and practically feasible estimation procedure for the index coefficients and the induced survival function. In particular, compared with the existing pseudo-likelihood approaches, our one proposes an automatic bandwidth selection and suppresses an influence of outliers. By making an effective use of the considered versatile survival process, we further reduce a substantial finite-sample bias in the Chambless-Diao type estimator of the most popular time-dependent accuracy summary. The asymptotic properties of estimators and data-driven bandwidths are also established under some suitable conditions. It is found in simulations that the proposed estimators and inference procedures exhibit quite satisfactory performances. Moreover, the general applicability of our methodology is illustrated by two empirical data.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0600-6
Issue No: Vol. 70, No. 3 (2018)

• Adaptive varying-coefficient linear quantile model: a profiled estimating
equations approach
• Authors: Weihua Zhao; Jianbo Li; Heng Lian
Pages: 553 - 582
Abstract: We consider an estimating equations approach to parameter estimation in adaptive varying-coefficient linear quantile model. We propose estimating equations for the index vector of the model in which the unknown nonparametric functions are estimated by minimizing the check loss function, resulting in a profiled approach. The estimating equations have a bias-corrected form that makes undersmoothing of the nonparametric part unnecessary. The estimating equations approach makes it possible to obtain the estimates using a simple fixed-point algorithm. We establish asymptotic properties of the estimator using empirical process theory, with additional complication due to the nuisance nonparametric part. The finite sample performance of the new model is illustrated using simulation studies and a forest fire dataset.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0599-8
Issue No: Vol. 70, No. 3 (2018)

• Statistical inferences based on INID progressively type II censored
order statistics
• Authors: M. Razmkhah; S. Simriz
Pages: 583 - 604
Abstract: Suppose that the failure times of the units placed on a life-testing experiment are independent but nonidentically distributed random variables. Under progressively type II censoring scheme, distributional properties of the proposed random variables are presented and some inferences are made. Assuming that the random variables come from a proportional hazard rate model, the formulas are simplified and also the amount of Fisher information about the common parameters of this family is calculated. The results are also extended to a fixed covariates model. The performance of the proposed procedure is investigated via a real data set. Some numerical computations are also presented to study the effect of the proportionality rates in view of the Fisher information criterion. Finally, some concluding remarks are stated.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0598-9
Issue No: Vol. 70, No. 3 (2018)

• Bootstrap inference for misspecified moment condition models
• Authors: Mihai Giurcanu; Brett Presnell
Pages: 605 - 630
Abstract: We study the standard-bootstrap, the centered-bootstrap, and the empirical-likelihood bootstrap tests of hypotheses used in conjunction with generalized method of moments inference in correctly specified and misspecified moment condition models. We show that, under correct specification, the standard-bootstrap estimator of the null distribution of the J-test converges in distribution to a random distribution, verifying its inconsistency, while the centered and the empirical-likelihood bootstrap estimators are consistent. We provide higher-order expansions of the size distortions of the analytic and the bootstrap tests. We show that the standard-bootstrap parameter-tests are consistent under misspecification, while the centered-bootstrap parameter-tests are inconsistent. We propose a general bootstrap methodology which is highly accurate under correct specification and consistent under misspecification. In a simulation study, we explore the finite sample behavior of the analytic and the bootstrap tests for a panel data model and we apply our methodology on a real-world data set.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0604-2
Issue No: Vol. 70, No. 3 (2018)

• Flexible sliced designs for computer experiments
• Authors: Xiangshun Kong; Mingyao Ai; Kwok Leung Tsui
Pages: 631 - 646
Abstract: Sliced Latin hypercube designs are popularly adopted for computer experiments with qualitative factors. Previous constructions require the sizes of different slices to be identical. Here we construct sliced designs with flexible sizes of slices. Besides achieving desirable one-dimensional uniformity, flexible sliced designs (FSDs) constructed in this paper accommodate arbitrary sizes for different slices and cover ordinary sliced Latin hypercube designs as special cases. The sampling properties of FSDs are derived and a central limit theorem is established. It shows that any linear combination of the sample means from different models on slices follows an asymptotic normal distribution. Some simulations compare FSDs with other sliced designs in collective evaluations of multiple computer models.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0603-3
Issue No: Vol. 70, No. 3 (2018)

• Multiscale inference for a multivariate density with applications to X-ray
astronomy
• Authors: Konstantin Eckle; Nicolai Bissantz; Holger Dette; Katharina Proksch; Sabrina Einecke
Pages: 647 - 689
Abstract: In this paper, we propose methods for inference of the geometric features of a multivariate density. Our approach uses multiscale tests for the monotonicity of the density at arbitrary points in arbitrary directions. In particular, a significance test for a mode at a specific point is constructed. Moreover, we develop multiscale methods for identifying regions of monotonicity and a general procedure for detecting the modes of a multivariate density. It is shown that the latter method localizes the modes with an effectively optimal rate. The theoretical results are illustrated by means of a simulation study and a data example. The new method is applied to and motivated by the determination and verification of the position of high-energy sources from X-ray observations by the Swift satellite which is important for a multiwavelength analysis of objects such as Active Galactic Nuclei.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0605-1
Issue No: Vol. 70, No. 3 (2018)

• A more powerful test identifying the change in mean of functional data
• Authors: Buddhananda Banerjee; Satyaki Mazumder
Pages: 691 - 715
Abstract: An existence of change point in a sequence of temporally ordered functional data demands more attention in its statistical analysis to make a better use of it. Introducing a dynamic estimator of covariance kernel, we propose a new methodology for testing an existence of change in the mean of temporally ordered functional data. Though a similar estimator is used for the covariance in finite dimension, we introduce it for the independent and weakly dependent functional data in this context for the first time. From this viewpoint, the proposed estimator of covariance kernel is more natural one when the sequence of functional data may possess a change point. We prove that the proposed test statistics are asymptotically pivotal under the null hypothesis and consistent under the alternative. It is shown that our testing procedures outperform the existing ones in terms of power and provide satisfactory results when applied to real data.
PubDate: 2018-06-01
DOI: 10.1007/s10463-017-0606-0
Issue No: Vol. 70, No. 3 (2018)

• Discussion
• Authors: Chien-Yu Peng
Pages: 269 - 274
PubDate: 2018-04-01
DOI: 10.1007/s10463-017-0640-y
Issue No: Vol. 70, No. 2 (2018)

• Discussion on the paper by Professor Wu
• Authors: Ryo Yoshida
Pages: 275 - 278
PubDate: 2018-04-01
DOI: 10.1007/s10463-017-0641-x
Issue No: Vol. 70, No. 2 (2018)

• Rejoinder
• Authors: C. F. Jeff Wu
Pages: 279 - 281
PubDate: 2018-04-01
DOI: 10.1007/s10463-017-0639-4
Issue No: Vol. 70, No. 2 (2018)

• Model-free feature screening for ultrahigh-dimensional data conditional on
some variables
• Authors: Yi Liu; Qihua Wang
Pages: 283 - 301
Abstract: In this paper, the conditional distance correlation (CDC) is used as a measure of correlation to develop a conditional feature screening procedure given some significant variables for ultrahigh-dimensional data. The proposed procedure is model free and is called conditional distance correlation-sure independence screening (CDC-SIS for short). That is, we do not specify any model structure between the response and the predictors, which is appealing in some practical problems of ultrahigh-dimensional data analysis. The sure screening property of the CDC-SIS is proved and a simulation study was conducted to evaluate the finite sample performances. Real data analysis is used to illustrate the proposed method. The results indicate that CDC-SIS performs well.
PubDate: 2018-04-01
DOI: 10.1007/s10463-016-0597-2
Issue No: Vol. 70, No. 2 (2018)

• The continuous-time triangular Pólya process
• Authors: Chen Chen; Hosam Mahmoud
Pages: 303 - 321
Abstract: We study poissonized triangular (reducible) urns on two colors, which we take to be white and blue. We analyze the number of white and blue balls after a certain period of time has elapsed. We show that for balanced processes in this class, a different scaling is needed for each color to produce nontrivial limits, contrary to the distributions in the usual irreducible urns which only require the same scaling for both colors. The limit distributions (of the scaled variables) underlying triangular urns are Gamma. The technique we use couples partial differential equations with the method of moments applied in a bootstrapped manner to produce exact and asymptotic moments. For the dominant color, we get exact moments, while relaxing the balance condition. The exact moments include alternating signs and Stirling numbers of the second kind.
PubDate: 2018-04-01
DOI: 10.1007/s10463-016-0594-5
Issue No: Vol. 70, No. 2 (2018)

• An information criterion for model selection with missing data via
complete-data divergence
• Authors: Hidetoshi Shimodaira; Haruyoshi Maeda
Pages: 421 - 438
Abstract: We derive an information criterion to select a parametric model of complete-data distribution when only incomplete or partially observed data are available. Compared with AIC, our new criterion has an additional penalty term for missing data, which is expressed by the Fisher information matrices of complete data and incomplete data. We prove that our criterion is an asymptotically unbiased estimator of complete-data divergence, namely the expected Kullback–Leibler divergence between the true distribution and the estimated distribution for complete data, whereas AIC is that for the incomplete data. The additional penalty term of our criterion for missing data turns out to be only half the value of that in previously proposed information criteria PDIO and AICcd. The difference in the penalty term is attributed to the fact that our criterion is derived under a weaker assumption. A simulation study with the weaker assumption shows that our criterion is unbiased while the other two criteria are biased. In addition, we review the geometrical view of alternating minimizations of the EM algorithm. This geometrical view plays an important role in deriving our new criterion.
PubDate: 2018-04-01
DOI: 10.1007/s10463-016-0592-7
Issue No: Vol. 70, No. 2 (2018)

• Dimension reduction for kernel-assisted M-estimators with missing response
at random
• Authors: Lei Wang
Abstract: To obtain M-estimators of a response variable when the data are missing at random, we can construct three bias-corrected nonparametric estimating equations based on inverse probability weighting, mean imputation, and augmented inverse probability weighting approaches. However, when the dimension of covariate is not low, the estimation efficiency will be affected due to the curse of dimensionality. To address this issue, we propose a two-stage estimation procedure by using the dimension-reduced kernel estimators in conjunction with bias-corrected estimating equations. We show that the resulting three kernel-assisted estimating equations yield asymptotically equivalent M-estimators that achieve the desirable properties. The finite-sample performance of the proposed estimators for response mean, distribution function and quantile is studied through simulation, and an application to HIV-CD4 data set is also presented.
PubDate: 2018-04-25
DOI: 10.1007/s10463-018-0664-y

• Asymptotic properties of parallel Bayesian kernel density estimators
• Authors: Alexey Miroshnikov; Evgeny Savelev
Abstract: In this article, we perform an asymptotic analysis of Bayesian parallel kernel density estimators introduced by Neiswanger et al. (in: Proceedings of the thirtieth conference on uncertainty in artificial intelligence, AUAI Press, pp 623–632, 2014). We derive the asymptotic expansion of the mean integrated squared error for the full data posterior estimator and investigate the properties of asymptotically optimal bandwidth parameters. Our analysis demonstrates that partitioning data into subsets requires a non-trivial choice of bandwidth parameters that optimizes the estimation error.
PubDate: 2018-04-18
DOI: 10.1007/s10463-018-0662-0

• Testing for a $$\delta$$ δ -neighborhood of a generalized Pareto copula
• Authors: Stefan Aulbach; Michael Falk; Timo Fuller
Abstract: A multivariate distribution function F is in the max-domain of attraction of an extreme value distribution if and only if this is true for the copula corresponding to F and its univariate margins. Aulbach et al.  (Bernoulli 18(2), 455–475, 2012. https://doi.org/10.3150/10-BEJ343) have shown that a copula satisfies the extreme value condition if and only if the copula is tail equivalent to a generalized Pareto copula (GPC). In this paper, we propose a $$\chi ^2$$ -goodness-of-fit test in arbitrary dimension for testing whether a copula is in a certain neighborhood of a GPC. The test can be applied to stochastic processes as well to check whether the corresponding copula process is close to a generalized Pareto process. Since the p value of the proposed test is highly sensitive to a proper selection of a certain threshold, we also present graphical tools that make the decision, whether or not to reject the hypothesis, more comfortable.
PubDate: 2018-04-17
DOI: 10.1007/s10463-018-0657-x

• Asymptotic properties of the realized skewness and related statistics
• Authors: Yuta Koike; Zhi Liu
Abstract: The recent empirical works have pointed out that the realized skewness, which is the sample skewness of intraday high-frequency returns of a financial asset, serves as forecasting future returns in the cross section. Theoretically, the realized skewness is interpreted as the sample skewness of returns of a discretely observed semimartingale in a fixed interval. The aim of this paper is to investigate the asymptotic property of the realized skewness in such a framework. We also develop an estimation theory for the limiting characteristic of the realized skewness in a situation where measurement errors are present and sampling times are stochastic.
PubDate: 2018-04-17
DOI: 10.1007/s10463-018-0659-8

• Robust functional estimation in the multivariate partial linear model
• Authors: Michael Levine
Abstract: We consider the problem of adaptive estimation of the functional component in a partial linear model where the argument of the function is defined on a q-dimensional grid. Obtaining an adaptive estimator of this functional component is an important practical problem in econometrics where exact distributions of random errors and the parametric component are mostly unknown. An estimator of the functional component that is adaptive over the wide range of multivariate Besov classes and robust to a wide choice of distributions of the linear component and random errors is constructed. It is also shown that the same estimator is locally adaptive over the same range of Besov classes and robust over large collections of distributions of the linear component and random errors as well. At any fixed point, this estimator attains a local adaptive minimax rate.
PubDate: 2018-04-13
DOI: 10.1007/s10463-018-0661-1

• Weighted allocations, their concomitant-based estimators, and asymptotics
• Authors: Nadezhda Gribkova; Ričardas Zitikis
Abstract: Various members of the class of weighted insurance premiums and risk capital allocation rules have been researched from a number of perspectives. Corresponding formulas in the case of parametric families of distributions have been derived, and they have played a pivotal role when establishing parametric statistical inference in the area. Nonparametric inference results have also been derived in special cases such as the tail conditional expectation, distortion risk measure, and several members of the class of weighted premiums. For weighted allocation rules, however, nonparametric inference results have not yet been adequately developed. In the present paper, therefore, we put forward empirical estimators for the weighted allocation rules and establish their consistency and asymptotic normality under practically sound conditions. Intricate statistical considerations rely on the theory of induced order statistics, known as concomitants.
PubDate: 2018-04-07
DOI: 10.1007/s10463-018-0660-2

JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327

Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs