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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 22, Issue 6 - Nov 2015
Volume 22, Issue 5 - Sep 2015
Volume 22, Issue 4 - Jul 2015
Volume 22, Issue 3 - May 2015
Volume 22, Issue 2 - Mar 2015
Volume 22, Issue 1 - Jan 2015
Selecting the target year
A Review of Dose Finding Methods and Theory
Cheung, Ying Kuen ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 401~413
DOI : 10.5351/CSAM.2015.22.5.401
In this article, we review the statistical methods and theory for dose finding in early phase clinical trials, where the primary objective is to identify an acceptable dose for further clinical investigation. The dose finding literature is initially motivated by applications in phase I clinical trials, in which dose finding is often formulated as a percentile estimation problem. We will present some important phase I methods and give an update on new theoretical developments since a recent review by Cheung (2010), with an aim to cover a broader class of dose finding problems and to illustrate how the general dose finding theory may be applied to evaluate and improve a method. Specifically, we will illustrate theoretical techniques with some numerical results in the context of a phase I/II study that uses trinary toxicity/efficacy outcomes as basis of dose finding.
Common Feature Analysis of Economic Time Series: An Overview and Recent Developments
Centoni, Marco ; Cubadda, Gianluca ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 415~434
DOI : 10.5351/CSAM.2015.22.5.415
In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.
A Note on Complex Two-Phase Sampling with Different Sampling Units of Each Phase
Lee, Sang Eun ; Jin, Young ; Shin, Key-Il ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 435~443
DOI : 10.5351/CSAM.2015.22.5.435
Two phase sampling design is useful to increase estimation efficiency using deep stratification, improved non-response adjustment and reduced coverage bias. The same sampling units are commonly used for the first and the second phases in complex two-phase sampling design. In this paper we consider a sampling scheme where the first phase sampling units are clusters and the second phase sampling units are list samples. Using selected clusters in first phase requires that we list up elements in the selected clusters from the first phase and then use the list as a secondary sampling frame for the second phase sampling design. Then we select second phase samples from the listed sampling frame. We suggest an estimator based on the complex two-phase sampling design with different sampling units of each phase. Also the estimated variances of the estimator obtained by using classic and replication variance methods are considered and compared using simulation studies. For real data analysis, 2010 Korea Farm Household Economy Survey (KFHES) and 2011 Korea Agriculture Survey (KAS) are used.
Multivariate Rotation Design for Population Mean in Sampling on Successive Occasions
Priyanka, Kumari ; Mittal, Richa ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 445~462
DOI : 10.5351/CSAM.2015.22.5.445
This article deals with the problem of estimation of the population mean in presence of multi-auxiliary information in two occasion rotation sampling. A multivariate exponential ratio type estimator has been proposed to estimate population mean at current (second) occasion using information on p-additional auxiliary variates which are positively correlated to study variates. The theoretical properties of the proposed estimator are investigated along with the discussion of optimum replacement strategies. The worthiness of proposed estimator has been justified by comparing it to well-known recent estimators that exist in the literature of rotation sampling. Theoretical results are justified through empirical investigations and a detailed study has been done by taking different choices of the correlation coefficients. A simulation study has been conducted to show the practicability of the proposed estimator.
Stationary Bootstrapping for the Nonparametric AR-ARCH Model
Shin, Dong Wan ; Hwang, Eunju ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 463~473
DOI : 10.5351/CSAM.2015.22.5.463
We consider a nonparametric AR(1) model with nonparametric ARCH(1) errors. In order to estimate the unknown function of the ARCH part, we apply the stationary bootstrap procedure, which is characterized by geometrically distributed random length of bootstrap blocks and has the advantage of capturing the dependence structure of the original data. The proposed method is composed of four steps: the first step estimates the AR part by a typical kernel smoothing to calculate AR residuals, the second step estimates the ARCH part via the Nadaraya-Watson kernel from the AR residuals to compute ARCH residuals, the third step applies the stationary bootstrap procedure to the ARCH residuals, and the fourth step defines the stationary bootstrapped Nadaraya-Watson estimator for the ARCH function with the stationary bootstrapped residuals. We prove the asymptotic validity of the stationary bootstrap estimator for the unknown ARCH function by showing the same limiting distribution as the Nadaraya-Watson estimator in the second step.
Graphical Methods for the Sensitivity Analysis in Discriminant Analysis
Jang, Dae-Heung ; Anderson-Cook, Christine M. ; Kim, Youngil ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 475~485
DOI : 10.5351/CSAM.2015.22.5.475
Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretable compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern of the change.
Kullback-Leibler Information of Consecutive Order Statistics
Kim, Ilmun ; Park, Sangun ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 487~494
DOI : 10.5351/CSAM.2015.22.5.487
A calculation of the Kullback-Leibler information of consecutive order statistics is complicated because it depends on a multi-dimensional integral. Park (2014) discussed a representation of the Kullback-Leibler information of the first r order statistics in terms of the hazard function and simplified the r-fold integral to a single integral. In this paper, we first express the Kullback-Leibler information in terms of the reversed hazard function. Then we establish a generalized result of Park (2014) to an arbitrary consecutive order statistics. We derive a single integral form of the Kullback-Leibler information of an arbitrary block of order statistics; in addition, its relation to the Fisher information of order statistics is discussed with numerical examples provided.
Bootstrap-Based Test for Volatility Shifts in GARCH against Long-Range Dependence
Wang, Yu ; Park, Cheolwoo ; Lee, Taewook ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 495~506
DOI : 10.5351/CSAM.2015.22.5.495
Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.
A Note on Performance of Conditional Akaike Information Criteria in Linear Mixed Models
Lee, Yonghee ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 507~518
DOI : 10.5351/CSAM.2015.22.5.507
It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.
Adjustment of Control Limits for Geometric Charts
Kim, Byung Jun ; Lee, Jaeheon ;
Communications for Statistical Applications and Methods, volume 22, issue 5, 2015, Pages 519~530
DOI : 10.5351/CSAM.2015.22.5.519
The geometric chart has proven more effective than Shewhart p or np charts to monitor the proportion nonconforming in high-quality processes. Implementing a geometric chart commonly requires the assumption that the in-control proportion nonconforming is known or accurately estimated. However, accurate parameter estimation is very difficult and may require a larger sample size than that available in practice in high-quality process where the proportion of nonconforming items is very small. Thus, the error in the parameter estimation increases and may lead to deterioration in the performance of the control chart if a sample size is inadequate. We suggest adjusting the control limits in order to improve the performance when a sample size is insufficient to estimate the parameter. We propose a linear function for the adjustment constant, which is a function of the sample size, the number of nonconforming items in a sample, and the false alarm rate. We also compare the performance of the geometric charts without and with adjustment using the expected value of the average run length (ARL) and the standard deviation of the ARL (SDARL).