• Korean Journal of Culture and Social Issue
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• v.16 no.4
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• pp.447-468
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• 2010

The purpose of this study was to attain a clearer understanding of the reciprocal effects of deviant self-concept and delinquent behaviors by applying a latent state-trait autoregressive modeling approach. Although traditional autoregressive cross-lagged (ARCL) modeling has been widely applied to test the longitudinal reciprocal relationship between the two constructs, it could produce misspecified findings if there were trait-like processes involved in this relationship. The latent state-trait autoregressive(LST-AR) modeling was applied to control trait effects of deviant self-concept and to examine the reciprocal causal relations between the two constructs. Data were taken from a sample of 3,449 eighth graders who were followed annually for 5 years from the Korea Youth Panel Study. The combining LST-AR model with ARCL model substantiated the reciprocal effects of deviant self-concept and delinquent behaviors, even after the stable trait component of deviant self-concept was taken into account. The present findings shed lights on the reciprocal effects of behaviors (i.e., delinquency) and self concepts (i.e., deviant self-concept). Not only did behaviors change corresponding self-concept, but the ways adolescents perceived themselves influenced their behaviors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.101-112
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• 1999

In this paper variable bit rate VBR Moving Picture Experts Group (MPEG) coded full-motion video traffic is modeled by a nonlinear time-series process. The threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched between amplitude sub-regions. To model the dynamics of the switching between the sub-regions a selection of amplitude dependent thresholds and a delay value is required. To this end, an efficient and accurate TAR model construction algorithm is developed to model VBR MPEG-coded video traffic. The TAR model is shown to accurately represent statistical characteristics of the actual full-motion video trace. Furthermore. in simulations for the bit-loss rate actual and TAR traces show good agreement.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.245-251
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• 2014

Two exponential inequalities are established for a wide class of general weakly dependent sequences of random variables, called ${\psi}$-weakly dependent process which unify weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. The ${\psi}$-weakly dependent process includes, for examples, stationary ARMA processes, bilinear processes, and threshold autoregressive processes, and includes essentially all classes of weakly dependent stationary processes of interest in statistics under natural conditions on the process parameters. The two exponential inequalities are established on more general conditions than some existing ones, and are proven in simpler ways.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.809-816
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• 2011

A tree-indexed autoregressive(AR) process is a time series defined on a tree which is generated by a branching process and/or a deterministic splitting mechanism. This short article is concerned with conditional heteroscedastic structure of the tree-indexed AR models. It has been usual in the literature to analyze conditional mean structure (rather than conditional variance) of tree-indexed AR models. This article pursues to identify quadratic conditional heteroscedasticity inherent in various tree-indexed AR models in a unified way, and thus providing some perspectives to the future works in this area. The identical conditional variance of sisters sharing the same mother will be referred to as the branching heteroscedasticity(BH, for short). A quasilikelihood but preliminary estimation of the quadratic BH is discussed and relevant limit distributions are derived.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1137-1149
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• 2014

Since the bifurcating autoregressive (BAR) model was developed by Cowan and Staudte (1986) to analyze cell lineage data, a lot of research has been directed to BAR and its generalizations. Based mainly on the author's works, this paper is concerned with a contemporary review on the BAR in terms of an overview and perspectives. Specifically, bifurcating structure is extended to multi-cast tree and to branching tree structure. The AR(1) time series model of Cowan and Staudte (1986) is generalized to tree structured random processes. Branching correlations between individuals sharing the same parent are introduced and discussed. Various methods for estimating parameters and related asymptotics are also reviewed. Consequently, the paper aims to give a contemporary overview on the BAR model, providing some perspectives to the future works in this area.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.174-179
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• 1998

This research explores the application of the bootstrap methods to the construction of control limits for the x charts and the EWMA charts based on single observations with stationary autoregressive processes. The subsample means-based control chars in the presence autocorrelation are also considered. We use a technique for inferring confidence intervals using bootstrap, the percentile method. Simulation studies are conducted to compare the performance of the bootstrap method and that of standard method for constructing control charts under several conditions.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.126-135
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• 2003

A basic assumption in standard applications of control charts is that the observations are statistically independent. However, this assumption is often violated from processes in many industries. The presence of autocorrelation has a serious impact on the performance of control charts, causing a dramatic increase in the frequency of false alarms. This paper considers a process in which the observations can be modeled as a first order autoregressive(AR(1)) process, and develops (equation omitted) charts with the variable sample size(VSS) scheme for monitoring the mean of this process.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.565-572
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• 2001

This article explores the problem of ergodicity for the nonlinear autoregressive processes with ARCH structure in a very general setting. A sufficient condition for the geometric ergodicity of the model is developed along the lines of Feigin and Tweedie(1985), thereby extending classical results for specific nonlinear time series. The condition suggested is in turn applied to some specific nonlinear time series illustrating that our results extend those in the literature.