Informal social networks in Korea: Covering the cost of major life events

This article investigates an informal voluntary social arrangement for financial assistance with discrete major life events known in Korea as Sang-Ho-Bu-Jo. This informal voluntary arrangement is neither public nor private, but is based on social networks that produce a unique form of civic society. Sang-Ho-Bu-Jo covers people's transitional one-time needs. This study explores the practice of Sang-Ho-Bu-Jo and its origins and provides the first systematic empirical study of this social phenomenon. Three large databases are used to analyze the scope and level of participation, as well as what variables correlate with such involvement. The study finds a high rate of participation, over 80% of households, and an average investment of 2–4% of household expenditure. Sang-Ho-Bu-Jo can help scholars and policy makers worldwide in understanding the role of socialization, social networks, and social capital in explaining innovative informal methods of social care.     

Minimum density power divergence estimator for covariance matrix based on skew $$t$$ t distribution

Statistical Methods & Applications - In this paper, we study the problem of estimating the covariance matrix of stationary multivariate time series based on the minimum density power divergence...     

A pragmatic algorithm for the train-set routing: The case of Korea high-speed railway

This paper presents a two-phased train-set routing algorithm to cover a weekly train timetable with minimal working days of a minimal number of train-sets. First, relax maintenance requirements and obtain minimum cost routes by solving the polynomial relaxation. Then, maintenance-feasible routes are generated from the crossovers of the minimum cost routes. This pragmatic approach seems particularly effective for the high-speed railway systems, where the railway topology is relatively simple with few end stations while the trains are frequent. Applied to the current weekly timetable of the Korea high-speed railway, we could find an optimal feasible routing, which is an 8.8% improvement over the current routing generated by a set partitioning approach based on a path generation scheme.     

On conditional correlations defined by a class of nonlinearly truncated distributions and their applications

This article derives and studies several types of conditional correlations. The correlations are obtained by a class of two-piece scale mixture skew-normal distributions. The class is obtained by applying a set of nonlinear constraints to the bivariate scale mixture of normal distributions. The correlations of the class are invariant with respect to the choice of the scale mixing function, however, they are dependent upon the type of the nonlinear truncation. Moreover, their respective upper and lower limits are no longer 1.00 and?1.00. They are useful for the truncated data analysis, the multivariate interdependence methods (such as the principal component analysis and the factor analysis), and the random truncation modelling. Some distributional properties and the Bayesian computation of the correlations are considered when developing necessary theories and providing illustrative examples, respectively. Two applications are also given to demonstrate the usefulness of the conditional correlations in a multivariate analysis.     

Logrank test for bivariate survival data

The logrank test procedure for testing bivariate symmetry against asymmetry in matched-pair data is proposed. The presented test statistic is based on Mantel-Haenszel type statistics evaluated at diagonal grid points on the plane obtained from distinct uncensored failure times. The asymptotic results of the proposed test are derived and an example is shown to illustrate the methodology.     

Analysis of Panel Count Data with Dependent Observation Times

In this article, a semiparametric approach is proposed for the regression analysis of panel count data. Panel count data commonly arise in clinical trials and demographical studies where the response variable is the number of multiple recurrences of the event of interest and observation times are not fixed, varying from subject to subject. It is assumed that two processes exist in this data: the first is for a recurrent event and the second is for observation time. Many studies have been done to estimate mean function and regression parameters under the independency between recurrent event process and observation time process. In this article, the same statistical inference is studied, but the situation where these two processes may be related is also considered. The mixed Poisson process is applied for the recurrent event processes, and a frailty intensity function for the observation time is also used, respectively. Simulation studies are conducted to study the performance of the suggested methods. The bladder tumor data are applied to compare previous studie' results.     

One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes

In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residuals—the difference between the actual value and the fitted value—with the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T ² chart.     

Estimation of the derivative of a density and related functions

Let Y₁,…,Y _n, (Y₁ <Y₂<…<Y _n) be the order statistics of a random sample from a distribution F with density f on the realline. This paper gives a class of estimators of the derivativef'(x) of the density f at points x for which f has

a continuoussecond derivative. These estimators are based on spacings inthe order statistics Y_j+kn -y _j j = 1,…,n-k_n,k_n<n.     

On the problem of selecting good populations

The problem of selecting good populations out of k normal populations is considered in a Bayesian framework under exchangeable normal priors and additive loss functions. Some basic approximations to the Bayes rules are discussed. These approximations suggest that some well-known classical rules are "approximate" Bayes rules. Especially, it is shown that Gupta-type rules are extended Bayes with respect to a family of the exchangeable normal priors for any bounded and additive loss function. Furthermore, for a simple loss function, the results of a Monte Carlo comparison of Gupta-type rules and Seal-type rules are presented. They indicate that, in general, Gupta-type rules perform better than Seal-type rules