Survivalist Modernity and the Logic of Its Governmentality

In this article, I analyze the governmental discourses of Chung-Hee Park, drawing on Walter Benjamin's concept of the archeology of dreams. Following Benjamin, I argue that modern Korea has been shaped largely under the prevailing influence of survivalism, viewed as a principle of governmentality as well as a collective mentality. Since the late 19th century the Korean peninsula has been enmeshed in enduring geopolitico-economic threats. The Cold War brought about suffering through the Korean War, which was carried over into an authoritarian and developmental state. Finally, under the hegemony of neoliberalism at the end of the 20th century, Koreans once again underwent a collective crisis of survival. Here, I focus on Cold War survivalism and explore the discourses of Chung-Hee Park as representative texts exemplifying the logic and rationale of survivalist governmentality. Viewed as the most influential figure of 20th-century Korea, Park crystallized and implemented a survivalist governmentality, the fundamental logic of which can be delineated in the following four axioms: (i) the transcendental structure of problems, (ii) kairotic time, (iii) the praxeology of power, and (iv) the sovereign leader. These four constitute the specific narrative of national survival that functioned as a blueprint for building up the developmental Korean state.     

Economies of scale,bulk discounts,and liquidity constraints: comparing unit value and transaction level evidence in a poor country

We examine bulk discounts, which are claimed to explain the Deaton and Paxson puzzle about household size and food demand, and which may matter to household behavior studied in other literatures. Most previous studies use unit values, which are subject to several biases and reflect economizing choices made by households, so may not reliably estimate the bulk discount schedule. Instead, individual transaction records in household expenditure diaries are used, which report expenditure, quantity, brand, unit size and number purchased per transaction. The bulk discount schedule is estimated for four foods (rice, canned meat, canned fish and chicken) that make up one-third of the total food budget in a survey in urban Papua New Guinea. For each food we use the dominant brand(s) so there is no quality variation and the estimated price schedule only reflects discounts due to variations in purchase quantity. All foods have precisely measured but small elasticities of unit price with respect to quantity purchased.     

Econometric Time Series Analysis Software: A Review

This article assesses the impact that the late W. Edwards Deming had on statistical practice in general and industrial statistics in particular. It provides a direct-from-the-trenches comparison of Deming's goals with their realizations and a commentary on the relevance and challenges of Deming's concepts to today's industrial environment.     

A class of weighted multivariate distributions related to doubly truncated multivariate t-distribution

This article introduces a class of weighted multivariate t-distributions, which includes the multivariate generalized Student t and multivariate skew t as its special members. This class is defined as the marginal distribution of a doubly truncated multivariate generalized Student t-distribution and studied from several aspects such as weighting of probability density functions, inequality constrained multivariate Student t-distributions, scale mixtures of multivariate normal and probabilistic representations. The relationships among these aspects are given, and various properties of the class are also discussed. Necessary theories and two applications are provided.     

Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection

In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates the sample selection scheme into a stochastic monotone trend constraint, variable selection, and robustness against departures from the normality assumption. We present the basic theoretical properties of the proposed model that include its stochastic representation, sample selection bias quantification, and hierarchical model specification to deal with the stochastic monotone trend constraint in the nonparametric component, simple bias corrected estimation, and variable selection for the linear components. We then develop computationally feasible Markov chain Monte Carlo methods for semiparametric Bernstein polynomial functions with stochastically constrained parameter estimation and variable selection procedures. We demonstrate the finite-sample performance of the proposed model compared to existing methods using simulation studies and illustrate its use based on two real data applications.     

Optimal asymmetric classification procedures for interval-screened normal data

Statistical methods for an asymmetric normal classification do not adapt well to the situations where the population distributions are perturbed by an interval-screening scheme. This paper explores methods for providing an optimal classification of future samples in this situation. The properties of the screened population distributions are considered and two optimal regions for classifying the future samples are obtained. These developments yield yet other rules for the interval-screened asymmetric normal classification. The rules are studied from several aspects such as the probability of misclassification, robustness, and estimation of the rules. The investigation of the performance of the rules as well as the illustration of the screened classification idea, using two numerical examples, is also considered.     

Analysis of Survival Data with Group Lasso

Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariates effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.     

Robust Regression Using Data Partitioning and M-Estimation

We propose a new robust regression estimator using data partition technique and M estimation (DPM). The data partition technique is designed to define a small fixed number of subsets of the partitioned data set and to produce corresponding ordinary least square (OLS) fits in each subset, contrary to the resampling technique of existing robust estimators such as the least trimmed squares estimator. The proposed estimator shares a common strategy with the median ball algorithm estimator that is obtained from the OLS trial fits only on a fixed number of subsets of the data. We examine performance of the DPM estimator in the eleven challenging data sets and simulation studies. We also compare the DPM with the five commonly used robust estimators using empirical convergence rates relative to the OLS for clean data, robustness through mean squared error and bias, masking and swamping probabilities, the ability of detecting the known outliers, and the regression and affine equivariances.     

Regression Analysis of Bivariate Current Status Data Using a Multistate Model

We consider bivariate current status data with death which often occur in animal tumorigenicity experiments. Instead of observing exact tumor onset time, the existence of tumor is known at death time or sacrifice time. Such an incomplete data structure makes it difficult to investigate the effect of treatment on tumor onset times. Furthermore, when tumor onsets occur at two sites, information for the order of their onsets is unknown. A multistate model is applied to incorporate the sequential occurrence of events. For the inference of parameters, an EM algorithm is applied and a real NTP (National Toxicology Program) dataset is analyzed as an illustrative example.     

Modeling Multilevel Survival Data Using Frailty Models

Often the dependence in multivariate survival data is modeled through an individual level effect called the frailty. Due to its mathematical simplicity, the gamma distribution is often used as the frailty distribution for hazard modeling. However, it is well known that the gamma frailty distribution has many drawbacks. For example, it weakens the effect of covariates. In addition, in the presence of a multilevel model, overall frailty comes from several levels. To overcome such drawbacks, more heavy-tailed distributions are needed to model the frailty distribution in order to incorporate extra variability. In this article, we develop a class of log-skew-t distributions for the frailty. This class includes the log-normal distribution along with many other heavy tailed distributions, e.g., log-Cauchy, log normal, and log-t as special cases.

Conditional on the frailty, the survival times are assumed to be independent with proportional hazard structure. The modeling process is then completed by assuming multilevel frailty-effects. Instead of tuning a strict parameterization of the baseline hazard function, we consider the partial likelihood approach and thus leave the baseline function unspecified. By eliminating the hazard, the pre-specification and computation are simplified considerably.