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  Subjects -> STATISTICS (Total: 130 journals)
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Journal of the Korean Statistical Society
Journal Prestige (SJR): 0.545
Citation Impact (citeScore): 1
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1226-3192 - ISSN (Online) 2005-2863
Published by Elsevier Homepage  [2906 journals]
  • A study of binomial AR(1) process with an alternative generalized binomial
           thinning operator

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      Abstract: Abstract In order to describe the finite-range integer-valued time series data with dependent structure and excess zeros, we introduce a new binomial AR(1) process with an alternative generalized binomial thinning operator. Some probabilistic and statistical properties of this model are derived. Model parameters are estimated by conditional least squares method and conditional maximum likelihood method. The consistency and asymptotic normality of these estimators are studied. In addition, a real data analysis shows a better performance of the proposed model than other existing models.
      PubDate: 2022-09-27
       
  • A new active zero set descent algorithm for least absolute deviation with
           generalized LASSO penalty

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      Abstract: Abstract A new active zero set descent algorithm is proposed for least absolute deviation (LAD) problems with generalized LASSO penalty. Zero set contains the terms in the cost function that are zero-valued at the solution. Unlike state-of-art numerical approximation strategies such as interior point method, user-chosen threshold value is not required by the proposed algorithm to identify the zero set. Moreover, no nested iteration is needed. The algorithm updates the zero set and basis search directions recursively until optimality conditions are satisfied. It is also shown that the proposed algorithm converges in finitely many steps. Extensive simulation studies and real data analysis are conducted to confirm the time-efficiency of our algorithm.
      PubDate: 2022-09-20
       
  • The phoropter method: a stochastic graphical procedure for prior
           elicitation in univariate data models

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      Abstract: Abstract Common methods for Bayesian prior elicitation call for expert belief in the form of numerical summaries. However, certain challenges remain with such strategies. Drawing on recent advances made in graphical inference, we propose an interactive method and tool for prior elicitation in which experts express their belief through a sequence of selections between pairs of graphics, reminiscent of the common procedure used during eye examinations. The graphics are based on synthetic datasets generated from underlying prior models with carefully chosen parameters, instead of the parameters themselves. At each step of the process, the expert is presented with two familiar graphics based on these datasets, billed as hypothetical future datasets, and makes a selection regarding their relative likelihood. Underneath, the parameters that are used to generate the datasets are generated in a way that mimics the Metropolis algorithm, with the experts’ responses forming transition probabilities. Using the general method, we develop procedures for data models used regularly in practice: Bernoulli, Poisson, and Normal, though it extends to additional univariate data models as well. A free, open-source Shiny application designed for these procedures is also available online, helping promote best practice recommendations in myriad ways. The method is supported by simulation.
      PubDate: 2022-09-19
       
  • Correction: Robust MAVE for single-index varying-coefficient models

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      PubDate: 2022-09-16
       
  • Improved multiple quantile regression estimation with nonignorable
           dropouts

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      Abstract: Abstract This paper proposes an efficient approach to deal with the issue of estimating multiple quantile regression (MQR) model. The relationship between the multiple quantiles and within-subject correlation is accommodated to improve efficiency in the presence of nonignorable dropouts. We adopt empirical likelihood (EL) to estimate the MQR coefficients. To handle the identifiability issue caused by nonignorable dropouts, a nonresponse instrument is used to estimate the parameters involved in a propensity model. In addition, bias-corrected and smoothed generalized estimating equations are built by applying kernel smoothing and inverse probability weighting approach. Furthermore, in order to measure the within-subject correlation structure, the idea of quadratic inference function is also taken into account. Theoretical results indicate that the proposed estimator has asymptotic normality and the confidence regions for MQR coefficients are also derived. Numerical simulations and an application to real data are also presented to investigate the performance of our proposed method.
      PubDate: 2022-09-08
       
  • Robust MAVE for single-index varying-coefficient models

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      Abstract: Abstract In this paper, a robust, efficient and easily implemented estimation procedure for single-index varying-coefficient models is proposed by combining minimum average variance estimation (MAVE) with exponential squared loss. The merit of the proposed method is robust against outliers or heavy-tailed error distributions while asymptotically efficient as the original MAVE under the normal error case. A practical minorization–maximization algorithm is proposed for implementation. Under some regularity conditions, asymptotic distributions of the resulting estimators are derived. Simulation studies and a real data example are conducted to examine the finite sample performance of the proposed method. Both theoretical and empirical findings confirm that our proposed method works very well.
      PubDate: 2022-09-02
       
  • Batch sequential adaptive designs for global optimization

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      Abstract: Abstract Efficient global optimization (EGO) is one of the most popular sequential adaptive design (SAD) methods for expensive black-box optimization problems. A well-recognized weakness of the original EGO in complex computer experiments is that it is serial, and hence the modern parallel computing techniques cannot be utilized to speed up the running of simulator experiments. For those multiple points EGO methods, the heavy computation and points clustering are the obstacles. In this work, a novel batch SAD method, named “Accelerated EGO”, is forwarded by using a refined sampling/importance resampling (SIR) method to search the points with large expected improvement (EI) values. The computation burden of the new method is much lighter, and the points clustering is also avoided. The efficiency of the proposed batch SAD is validated by nine classic test functions with dimension from 2 to 12. The empirical results show that the proposed algorithm indeed can parallelize original EGO, and gain much improvement compared against the other parallel EGO algorithm especially under high-dimensional case. Additionally, the new method is applied to the hyper-parameter tuning of support vector machine (SVM) and XGBoost models in machine learning. Accelerated EGO obtains comparable cross validation accuracy with other methods and the CPU time can be reduced a lot due to the parallel computation and sampling method.
      PubDate: 2022-09-01
       
  • Infinite diameter confidence sets in Hedges’ publication bias model

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      Abstract: Abstract Meta-analysis, the statistical analysis of results from separate studies, is a fundamental building block of science. But the assumptions of classical meta-analysis models are not satisfied whenever publication bias is present, which causes inconsistent parameter estimates. Hedges’ selection function model takes publication bias into account, but estimating and inferring with this model is tough for some datasets. Using a generalized Gleser–Hwang theorem, we show there is no confidence set of guaranteed finite diameter for the parameters of Hedges’ selection model. This result provides a partial explanation for why inference with Hedges’ selection model is fraught with difficulties.
      PubDate: 2022-09-01
       
  • Multivariate response regression with low-rank and generalized sparsity

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      Abstract: Abstract In this study, we propose a multivariate-response regression by imposing structural conditions on the underlying regression coefficient matrix motivated by an analysis of Cancer Cell Line Encyclopedia (CCLE) data consisting of resistance responses to multiple drugs and gene expression of cancer cell lines. It is important to estimate the drug resistance response from gene information and identify those genes responsible for the sensitivity of the resistance response to each drug. We consider a penalized multiple-response regression estimator using both generalized \(\ell _1\) norm and nuclear norm regularizers based on the motivations that only a few genes are relevant to the effect of drug resistance responses and that some genes could have similar effects on multiple responses. For the statistical properties, we developed non-asymptotic error bounds of the proposed estimator. In our numerical analysis using simulated and CCLE data, the proposed method better predicts the drug responses than the other methods.
      PubDate: 2022-09-01
       
  • A novel non-heuristic search technique for constructing uniform designs
           with a mixture of two- and four-level factors: a simple industrial
           applicable approach

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      Abstract: Abstract Uniformly scatter the design points over the experimental domain is one of the most widely used techniques to construct optimal designs (called, uniform designs) for real-world high-dimensional experiments with limited resources and without model pre-specification. Uniform designs are robust to the underlying model assumption and thus experimenters do not need to specify the models of their experiments in advance before conducting them. A uniform design affords a good design space coverage that yields more accurate approximations globally using fewer experimental trials. The construction of uniform designs is a significant challenge due to the computational complexity. The existing techniques are extremely time-consuming (heuristic search techniques), difficult for non-mathematicians experimenters, and optimal results are not guaranteed. This paper tries to help non-mathematicians experimenters by providing a simple non-heuristic search technique for constructing uniform designs for experiments with a mixture of two- and four-level factors. The efficiency of the new technique is investigated theoretically and numerically. A comparison study between the new technique and the existing techniques is given. Furthermore, the applicability of the new technique for real-world applications is discussed and demonstrated by two real industrial experiments. The results show that the new designs that are generated by the new technique are better than the existing recommended designs.
      PubDate: 2022-09-01
       
  • Asymptotic equivalence between frequentist and Bayesian prediction limits
           for the Poisson distribution

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      Abstract: Abstract Bayesian prediction limits are constructed based on some maximum allowed probability of wrong prediction. However, the frequency of wrong prediction in a long run often exceeds this probability. The literature on frequentist and Bayesian prediction limits, and their interpretation is sparse; more attention is given to prediction intervals obtained based on parameter estimates or empirical studies. Under the Poisson distribution, we investigate frequentist properties of Bayesian prediction limits derived from conjugate priors. The frequency of wrong prediction is used as a criterion for their comparison. Bayesian prediction based on the uniform and Jeffreys’ non-informative priors yield one sided prediction limits that can be interpreted in a frequentist context. It is shown here, by proving a theorem, that Bayesian lower prediction limit derived from Jeffreys’ noninformative prior is the only optimal (largest) Bayesian lower prediction limit that possesses frequentist properties. In addition, it is concluded as corollary that there is no prior distribution such that Bayesian upper and lower prediction limits obtained from it will both coincide with their respective frequentist prediction limits. Our results are based on asymptotic considerations. An example with real data is included, and the sensitivity of the Bayesian prediction limits with respect to conjugate priors is numerically explored through simulations.
      PubDate: 2022-09-01
       
  • Markov switching quantile regression models with time-varying transition
           probabilities

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      Abstract: Abstract Markov switching models are widely used in the time series field for their ability to describe the impact of latent regimes on the behaviour of response variables. Meanwhile, Markov switching quantile regression models with fixed transition probabilities (MSQR-FTP) also provide rich dynamics to modeling financial data, however, it is not always clear how to describe the dynamics on the transition probabilities. This paper extends the transition probabilities to be the time-varying case by allowing them to include information from related variables. By establishing a connection between a quantile regression and an asymmetric Laplace distribution, this paper proposes a maximum likelihood estimation (MLE) method for MSQR-TVTP, and shows the consistency of the MLE. Finally, the performance of the proposed method is illustrated through a simulation study. As an empirical application, we further apply the method to the S&P 500 weekly percentage returns.
      PubDate: 2022-09-01
       
  • Asymptotic approximations for some distributions of ratios

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      Abstract: Abstract We give strong large deviation results for some ratio distributions. Then we apply these results to two statistical examples: a ratio distribution with sums of gamma-distributed random variables and another one with sums of \(\chi ^2\) -distributed random variables. We eventually carry out numerical comparisons with a saddlepoint approximation using an indirect Edgeworth expansion and a Lugannani and Rice saddlepoint approximation.
      PubDate: 2022-09-01
       
  • Penalized relative error estimation of functional multiplicative
           regression models with locally sparse properties

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      Abstract: Abstract Based on functional relative errors, we develop a functional penalized smooth least absolute relative error (FPSLARE) criterion for locally sparse functional multiplicative regression models. Under some mild conditions, we establish the oracle properties of FPSLARE estimators, including the consistency of model selection and the asymptotic normality for the estimator. In addition, numerical studies and a real data analysis are carried out to evaluate the performance of the proposed approaches.
      PubDate: 2022-09-01
       
  • Evaluating the adequacy of variance function using pairwise distances

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      Abstract: Abstract In this article, we develop a distance-based testing for assessing the adequacy of conditional variance function using pairwise distances. Under the null hypothesis, we state the limiting distribution of the proposed statistic, which is complicated. A resampling type statistic is proposed for approximating the null distribution, and we prove the validity of the resampling algorithm. The test could detect any local alternatives at a nearly optimal rate. Simulation studies are provided to examine the numerical performance of the proposed test, and a real data example is illustrated its application.
      PubDate: 2022-09-01
       
  • Robust estimation and variable selection for varying-coefficient partially
           nonlinear models based on modal regression

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      Abstract: Abstract In this paper, we propose a robust two-stage estimation and variable selection procedure for varying-coefficient partially nonlinear model based on modal regression. In the first stage, each coefficient function is approximated by B-spline basis functions and then QR decomposition is employed to remove the nonparametric component from the original model. For the simple parametric model, an estimation and variable selection procedure for parameter is proposed based on modal regression. In the second stage, similar procedure for coefficient function is developed. The proposed procedure is not only flexible and easy to implement, but also is robust and efficient. Under some mild conditions, certain asymptotic properties of the resulting estimators are established. Moreover, the bandwidth selection and estimation algorithm for the proposed method is discussed. Furthermore, we conduct some simulations and a real example to evaluate the performances of the proposed estimation and variable selection procedure in finite samples.
      PubDate: 2022-09-01
       
  • Robust estimation of Gaussian linear structural equation models with equal
           error variances

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      Abstract: Abstract This study develops a new approach to learning Gaussian linear structural equation models (SEMs) with equal error variances from possibly corrupted observations by outliers. More precisely, we consider the two types of corrupted Gaussian linear SEMs depending on the outlier type and develop a structure learning algorithm for the models. The proposed algorithm consists of two steps in which the effect of outliers is eliminated: Step (1) infers the ordering using conditional variances, and Step (2) estimates the presence of edges using conditional independence relationships. Various numerical experiments verify that the proposed algorithm is empirically consistent even when corrupted samples exist. It is further confirmed that the proposed algorithm performs better than the state-of-the-art US, GDS, PC, and GES algorithms in noisy data settings. Through the corrupted real examination marks data, we also demonstrate that the proposed algorithm is well-suited to capturing the interpretable relationships between subjects.
      PubDate: 2022-09-01
       
  • Empirical likelihood confidence regions for autoregressive models with
           explanatory variables

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      Abstract: Abstract The empirical likelihood method is very useful for establishing confidence region of the parameters of interest. In this paper, an empirical likelihood confidence region for the parameters of a univariate AR(p) model with a single explanatory variable which enters in the model through a nonlinear function is studied. Since the analytical expression of the nonlinear function is unknown, it is replaced by a nonparametric estimator, which is subsequently plugged in the estimating equations for the parameters of interest (the autoregressive parameters) to obtain the empirical likelihood confidence region. Our approach is to establish an empirical likelihood ratio statistic which is asymptotically chi-squared distributed. Some simulation results are also presented to illustrate the performance of the empirical likelihood method. An application to a real data set is provided.
      PubDate: 2022-09-01
       
  • On robustness of the relative belief ratio and the strength of its
           evidence with respect to the geometric contamination prior

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      Abstract: Abstract The relative belief ratio becomes a widespread tool in many hypothesis testing problems. It measures the statistical evidence that a given statement is true based on a combination of data, model and prior. Additionally, a measure of the strength is used to calibrate its value. In this paper, robustness of the relative belief ratio and its strength to the choice of the prior is studied. Specifically, the Gâteaux derivative is used to measure their sensitivity when the geometric contaminated prior is used. Examples are presented to illustrate the results.
      PubDate: 2022-09-01
       
  • Statistical inference for Cox model under case-cohort design with subgroup
           survival information

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      Abstract: Abstract With the explosive growth of data, it is a challenge to infer the quantity of interest by combining the existing different research data about the same topic. In the case-cohort setting, our aim is to improve the efficiency of parameter estimation for Cox model by using subgroup information in the aggregate data. So we put forward the generalized moment method (GMM) to use the auxiliary survival information at some critical time points. However, the auxiliary information is likely obtained from other studies or populations, two extended GMM estimators are proposed to account for multiplicative and additive inconsistencies. We establish the consistency and asymptotic normality of the proposed estimators. In addition, the uniform consistency and asymptotic normality of Breslow estimator are also presented. From the asymptotic normality, we show that the proposed approaches are more efficient than the traditional weighted estimating equation method. In particular, if the number of subgroups is equal to one, the asymptotic variance-covariances of the GMM estimators are identical with the weighted score estimate. Some simulation studies and a real data study demonstrate the proposed methods and theories. In the numerical studies, our approaches are even better than the full cohort estimator and the extended GMM methods are robust.
      PubDate: 2022-09-01
       
 
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