Journal of the Korean Statistical Society最新文献

Nonparametric density estimation for nonnegative data is considered in a situation where a random sample is not directly available but the data are instead observed from the length-biased sampling. Due to the so-called boundary bias problem of the location-scale kernel, the approach in this paper is an application of asymmetric kernel. Some nonparametric density estimators are proposed. The mean integrated squared error, strong consistency, and asymptotic normality of the estimators are investigated. Simulation studies and a real data analysis illustrate the estimators.

The community detection is a significant problem in network data analysis. In this paper, we implement community detection by minimizing an objective function based on the difference between the adjacency matrix and its expected value, and explain the rationality of the objective function. To solve the optimization problem, we propose a new algorithm which is referred to the thoughts of Markov Chain Monte Carlo and low discrepancy sequence in the random simulation fields. We introduce a new indicator to compare the performance of the methods by measuring the similarity of the true community and the estimated community. Synthetic networks and real networks are analyzed to investigate the effectiveness of the new method. Results show that the performance of the proposed method is stable in all simulated scenarios. And in most cases, it outperforms existing methods.

We propose a new estimator of the integrated volatility in presence of observed noise variables, measured, for example, by the trading volume or the bid-ask-spread. We find that, under specific conditions, the proposed estimator is consistent and the error, adjusted for the noise effects, between the proposed estimator and the integrated volatility has the same asymptotic distribution of the realized volatility estimator under no noise effects. Finally, our results are validated by a simulation and an empirical study.

The importance of the back-end process in semiconductor manufacturing has recently received significant attention from global manufacturers. The analysis of manufacturing data often provides crucial insights into problems inherent in the manufacturing processes. An important goal of the back-end process is to improve the yield of final products, called packages. A simple way to achieve this goal is to characterize low-quality wafers based on the analysis of manufacturing data and discard them before proceeding to the packaging step. Alternatively, this paper proposes a novel packaging method that significantly improves the package yield using statistical models scoring the quality of dies. We prove that the proposed packaging method is optimal and conduct thorough numerical experiments, showing its superiority.

This paper introduces a powerful bias reduction technique applied to local linear additive regression. The main idea is to make use of a parametric family. Existing techniques based on this idea use a parametric model that is linear in the parameter. In this paper we generalize the approaches by allowing nonlinear parametric families. We develop the methodology and theory for response variables taking values in a general separable Hilbert space. Under mild conditions, our proposed approach not only offers flexibility but also gains bias reduction while maintaining the variance of the local linear additive regression estimators. We also provide numerical evidences that support our approach.

This article proposes objective Bayesian multiple testing procedures for a normal model. The challenging task of considering all the configurations of true and false null hypotheses is addressed here by ordering the null hypotheses based on their Bayes factors. This approach reduces the size of the compared models for posterior search from (2^k) to (k+1), for k null hypotheses. Furthermore, the consistency of the proposed multiple testing procedures is established and their behavior is analyzed with simulated and real examples. In addition, the proposed procedures are compared with classical and Bayesian multiple testing procedures in all the possible configurations of true and false ordered null hypotheses.

Missing data is prevalent in many fields. Among all missing mechanisms, nonignorable missing data is more challenging for model identification. In this paper, we propose a semiparametric regression model estimation method with nonignorable missing responses. To be specific, we first construct a parametric model for the propensity score and apply the generalized method of moments to obtain the estimated propensity score. For nonignorable missing responses, based on the inverse probability weighting approach, we propose the penalized garrotized kernel machine method to flexibly depict the complex nonlinear relationships between the response and the predictors, allow for interactions between the predictors, and eliminate the redundant variables automatically. The cyclical coordinate descent algorithm is provided to solve the corresponding optimization problems. Numerical results and real data analysis indicate that our proposed method achieves better prediction performance compared with the competing ones.

Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly with light detection and ranging (LiDAR) measurements. However, regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors.

In the era of big data, online updating problems have attracted extensive attention. In practice, the covariates set of the models may change according to the conditions of data streams. In this paper, we propose a two-stage online debiased lasso estimation and inference method for high-dimensional heterogenous linear regression models with new variables added midway. At the first stage, the homogenization strategy is conducted to represent the heterogenous models by defining the pseudo covariates and responses. At the second stage, we conduct the online debiased lasso estimation procedure to obtain the final estimator. Theoretically, the asymptotic normality of the heterogenous online debiased lasso estimator (HODL) is established. The finite-sample performance of the proposed estimators is studied through simulation studies and a real data example.

Motivated by different groups containing different group information under the heteroscedastic error structure, we propose the groupwise scaled envelope model that is invariable to scale changes and is permissible for distinct regression coefficients and the heteroscedastic error structure across groups. It retains the potential of the scaled envelope methods to keep the scale invariant and allows for both different regression coefficients and different error structures for diverse groups. Further, we demonstrate the maximum likelihood estimators and its theoretical properties including parameter identifiability, asymptotic distribution and consistency of the groupwise scaled envelope estimator. Lastly, simulation studies and a real-data example demonstrate the advantages of the groupwise scaled envelope estimators, including a comparison with the standard model estimators, groupwise envelope estimators, scaled envelope estimators and separate scaled envelope estimators.