Inferences on the Means of Two Log-Normal Distributions: A Computational Approach Test

In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

CAN THE PRESIDENT REALLY AFFECT ECONOMIC GROWTH? PRESIDENTIAL EFFORT AND THE POLITICAL BUSINESS CYCLE

Presidential elections are often seen as referendums on the health of the economy; however, little evidence exists on the president's ability to influence gross domestic product (GDP). This study examines the effect of the incentive to be reelected and the resulting increase in presidential effort on GDP growth. Growth is found to rise in reelection years for first‐term presidents after 1932 and to fall in election years before 1932, when reelection was uncommon, and for second‐term presidents generally. This effect is largest for high‐quality presidents—who probably have the highest return to effort—and is spread across multiple sectors of the economy. (JEL D78, D72, E32, J24)     

Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method

Abstract

In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.     

Cohabitation and commitment: partnership plans of young men and women

Popular commentators on marriage and the family often interpret the increase in heterosexual couples living together without marrying as reduced willingness to create and honour life‐long partnerships. Survey and in‐depth interviews with samples of 20–29 year olds living in an urban area of Scotland finds little support for the postulated link between growing cohabitation and a weakened sense of commitment to long‐term arrangements. Most of the cohabiting couples strongly stressed their ‘commitment’. Socially acceptable vocabularies of motive undoubtedly influenced answers but interviews helped to explore deeper meanings. Many respondents’ views were consistent with previous research predictions of a weakening sense of any added value of marriage. At the same time, some respondents continued to stress the social significance of the distinction between marriage and cohabitation, consistent with research interpreting cohabitation as a ‘try and see’ strategy part‐way to the perceived full commitment of marriage. The notion that ‘marriage is better for children’ continued to have support among respondents. While, on average, cohabiting couples had lower incomes and poorer employment situations than married couples, only very extreme adverse circumstances were presented as making marriage ‘too risky’. Pregnancy‐provoked cohabitation was not always in this category. Cohabitation was maintained because marriage would ‘make no difference’ or because they ‘had not yet got round to’ marriage. Most respondents were more wary of attempting to schedule or plan in their personal life than in other domains and cohabitees’ attitudes to partnership, including their generally ‘committed’ approach, do not explain the known greater vulnerability of this group to dissolution.