Ethnicity and fertility desires in Ghana

Journal of Population Research - The extant literature shows that fertility desires are an important indicator for understanding and predicting the future course of fertility; however, little work...     

Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects

The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients'' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called ‘HLnet’) in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.     

An elastic-net penalized expectile regression with applications

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.     

Industrial Policy Capture in Taiwan and South Korea

Whatever the theoretical merits of an industrial policy, in practice state incentives in support of infant industry are difficult to withdraw once they have been granted. Infant maturation is thereby delayed, resulting in ‘policy capture’ which confers rents on the assisted firms at the expense of consumers and/or taxpayers. The process is well-documented in poorly managed economies pursuing autarkic industrial policies (Auty, 1994a). This article examines evidence of policy capture in well-managed economies pursuing competitive industrial policies. Examples are drawn from the heavy (and chemical) industry drives of South Korea (hereafter Korea) and Taiwan, but the research is first set in the context of the industrial policy debate.     

The Effects of Personality Type on Choices Made in Strategic Decision Situations*

Jung's personality-theory typology is used as a framework for exploring the effects of cognitive style on the type and radicalness of choices made in strategic decision situations. Extending the work of Haley and Stumpf [23], it is proposed that individuals with different personality-type preferences exhibit cognitive styles that are associated with specific biases in the pattern of choices they make. Through participation in an interactive behavioral simulation, 407 participants confronted over one hundred ill-structured decision situations and proposed whatever actions they perceived appropriate. The results support the hypothesized relationships that individuals with different personality-type preferences (i.e., sensing-thinking, intuition-thinking, sensing-feeling, and intuition-feeling) take patterns of actions that reflect specific biases (i.e., selective perception, positivity, social desirability, and reasoning-by-analogy, respectively). The implications of these findings for evaluating the likely effectiveness of strategic decisions and making senior-level staffing decisions are discussed.     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

A Comment on Second-Order Polynomial Regression Models

In a recent article, Chatterjee and Greenwood [1] addressed the problem of multicollinearity in polynomial regression models. They noted that there is a high correlation between X and X²; therefore, a second-order polynomial model suffers the consequences of collinearity. Chatterjee and Greenwood [1] suggested a method they believe will overcome the problem. The contention of the present comment is that the suggested method accomplishes nothing and, indeed, has the potential to lead the unwary researcher to the wrong inference and misinterpretation of his results.     

Some Comments on the Validity of EOQ Formula under Inflationary Conditions

In an earlier issue of Decision Sciences, Jesse, Mitra, and Cox [1] examined the impact of inflationary conditions on the economic order quantity (EOQ) formula. Specifically, the authors analyzed the effect of inflation on order quantity decisions by means of a model that takes into account both inflationary trends and time discounting (over an infinite time horizon). In their analysis, the authors utilized two models: Current-dollars model and Constant-dollars model. These models were derived, of course, by setting up a total cost equation in the usual manner then finding the optimum order quantity that minimizes the total cost. Jesse, Mitra, and Cox [1] found that EOQ is approximately the same under both conditions; with or without inflation. However, we disagree with the conclusion drawn by [2] and show that EOQ will be different under inflationary conditions, provided that the inflationary conditions are properly accounted for in the formulation of the total cost model.     

Pearson-Tukey Three-Point Approximations Versus Monte Carlo Simulation

Accuracy of the Pearson-Tukey three-point approximation is measured in units of standard deviation and compared with that of Monte Carlo simulation. Using a variety of well-known distributions, comparisons are made for the mean of a random variable and for common functions of one and two random variables. Comparisons are also made for the mean of an assortment of risk-analysis (Monte Carlo) models drawn from the literature. The results suggest that the Pearson-Tukey approximation is a useful alternative to simulation in risk-analysis situations.     

Regional Differentials in Unemployment Rates: The Economic Causes

Abstract. Recent studies on the causes that bring about the increase in unemployment rates of the Mezzogiorno area and Italy are mainly based upon demographic and social causes. The aim of this study is to verify if they can be also influenced by economic factors. Through a dynamic model with partial adaptive hypothesis, some ways by which demand affects the differences of regional unemployment rates has been tested. In this respect it has been considered whether variations in economic activity can lead to different growth rates of regional production owing to the different industrial structures; whether the employers are influenced by local factors in varying the occupation when the production increases, and finally in which way the regional unemployment rate depends on the excess of desired, over effective, labour demand. Data base are from 1959 to 1976, and the unemployment rate is referred only to unemployed workers that is the better proxy of the unemployment connected with the economic cycle. The results show a slight reactiveness to demand, both in industrial labour demand and supply in the Mezzogiorno.