• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.209-222
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• 2015

Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.95-103
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• 2015

We introduce a surplus process which follows a diffusion process with positive drift and is subject to two types of claim. We assume that type I claim occurs more frequently, however, its size is stochastically smaller than type II claim. We obtain the ruin probability that the level of the surplus becomes negative, and then, decompose the ruin probability into three parts, two ruin probabilities caused by each type of claim and the probability that the level of the surplus becomes negative naturally due to the diffusion process. Finally, we illustrate a numerical example, when the sizes of both types of claim are exponentially distributed, to compare the impacts of two types of claim on the ruin probability of the surplus along with that of the diffusion process.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.69-80
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• 2015

In this paper, the probability that a given point is covered by a random convex hull generated by independent and identically-distributed random points in a plane is studied. It is shown that such probability can be expressed in terms of an integral that can be approximated numerically by function-evaluations over the grid-points in a 2-dimensional space. The new integral representation allows such probability be computed efficiently. The computational burdens under the proposed integral representation and those in the existing literature are compared. The proposed method is illustrated through numerical examples where the random points are drawn from (i) uniform distribution over a square and (ii) bivariate normal distribution over the two-dimensional Euclidean space. The applications of the proposed method in statistics are are discussed.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.31-40
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• 2015

This study proposes a mathematical model that can forecast national defense expenditures. The ongoing European debt crisis weighs heavily on markets; consequently, government spending in many countries will be constrained. However, a forecasting model to predict military spending is acutely needed for South Korea because security threats still exist and the estimation of military spending at a reasonable level is closely related to economic growth. This study establishes two models: an Auto-Regressive Moving Average model (ARIMA) based on past military expenditures and Transfer Function model with the Gross Domestic Product (GDP), exchange rate and consumer price index as input time series. The proposed models use defense spending data as of 2012 to create defense expenditure forecasts up to 2025.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.11-14
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• 2015

The Great Famine may have had a continued impact on the population structure of North Korea even after the crisis subsided ten years ago. However, there is a significant gap between what has been said about the country and what data indicates. This gap seems inevitable mainly because reliable data are seriously lacking and access is restricted for most scholars outside the country. Yet, it is only reasonable to question why most studies have failed to explain the causality between the Great Famine and accumulated changes in the population of North Korea. In this regard, a recent study conducted by Korean demographers (Jeon et al., 2015) have several implications on the importance of accurate and reliable data when the study involves such rare and scarce information. This paper explores the changing trends of the population structure in North Korea providing a review of recent studies on demographic issues associated with North Korea and offers suggestions on understanding the post-famine effect on the overall changes in the population of North Korea.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.15-18
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• 2015

Reunification, (considered a 'bonanza'), is often expected to bring economic and social benefits as well as solutions to the population aging problem; consequently, the study on the population structure of North Korea is salient. This paper reviews and talks on the North Korean population and implications. In view of reliability and consistency, the North Korean population appears at an explicit change compared to the past, and shows significant differences from the South, implying that it needs significant resources to integrate during reunification. Therefore, it is opportune to discuss the North Korean population prior to the clamor for the reunification.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.1-10
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• 2015

The re-unification of the two Koreas is seen as a potential solution to the aging problem and low fertility, along with the increase in the old population structure of the Republic of Korea. Population structure is an indicator of national competitiveness or growth, but little is known officially about the populations of Democratic People's Republic of Korea (DPRK). Understanding the population structure of the DPRK is important to prepare for re-unification and estimate the socio-economic costs of national welfare under a re-unified Korea. In this paper, we assess reliability of the two modern national censuses of DPRK and use the limited resources available to reconstruct the intercensal populations between these two censuses. Excess deaths from the 1995 famine are estimated at 489,972 to 574,306 and are close to the estimates of Goodkind et al. (2011) and the reconstructed populations in the of DPRK implies a big difference between two Koreas.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.295-304
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• 2015

This paper considers the problem of estimation of the Hurst parameter H ${\in}$ (1/2, 1) from longitudinal data with the error term of a fractional Brownian motion with Hurst parameter H that gives the amount of the long memory of its increment. We provide a new estimator of Hurst parameter H using a two scale sampling method based on $A{\ddot{i}}t$-Sahalia and Jacod (2009). Asymptotic behaviors (consistent and central limit theorem) of the proposed estimator will be investigated. For the proof of a central limit theorem, we use recent results on necessary and sufficient conditions for multi-dimensional vectors of multiple stochastic integrals to converges in distribution to multivariate normal distribution studied by Nourdin et al. (2010), Nualart and Ortiz-Latorre (2008), and Peccati and Tudor (2005).