A reanalysis of a longitudinal scleroderma clinical trial using non-ignorable missingness models

When analyzing incomplete longitudinal clinical trial data, it is often inappropriate to assume that the occurrence of missingness is at random, especially in cases where visits are entirely missed. We present a framework that simultaneously models multivariate incomplete longitudinal data and a non-ignorable missingness mechanism using a Bayesian approach. A criterion measure is presented for comparing models. We demonstrate the feasibility of the methodology through reanalysis of two of the longitudinal measures from a clinical trial of penicillamine treatment for scleroderma patients. We compare the results for univariate and bivariate, ignorable and non-ignorable missingness models.     

IG-symmetry and R-symmetry: Interrelations and applications to the inverse Gaussian theory

The two parameter inverse Gaussian (IG) distribution is often more appropriate and convenient for modelling and analysis of nonnegative right skewed data than the better known and now ubiquitous Gaussian distribution. Its convenience stems from its analytic simplicity and the striking similarities of its methodologies with those employed with the normal theory models. These, known as the G–IG analogies, include the concepts and measures of IG-symmetry, IG-skewness and IG-kurtosis, the IG-analogues of the corresponding classical notions and measures. The new IG-associated entities, although well defined and mathematically transparent, are intuitively and conceptually opaque. In this paper, we first elaborate the importance of the IG distribution and of the G–IG analogies. Then we consider the IG-related root-reciprocal IG (RRIG) distribution and introduce a physically transparent, conceptually clear notion of reciprocal symmetry (R-symmetry) and use it to explain the IG-symmetry. We study the moments and mixture properties of the R-symmetric distributions and the relationship of R-symmetry with IG-symmetry and note that RRIG distribution provides a link, in addition to Tweedie's Laplace transform link, between the Gaussian and inverse Gaussian distributions. We also give a structural characterization of the unimodal R-symmetric distributions. This work further expands the long list of G–IG analogies. Several applications including product convolution, monotonicity of power functions, peakedness and monotone limit theorems of R-symmetry are outlined.     

近十年西北近代农业经济发展研究综述

西北近代农业经济发展是西北近代经济史的一个重要组成部分,研究者对西北近代农业经济发展的研究越来越重视,并取得了丰硕的研究成果。基于目前的研究现状与规模,主要从通论、农业科技的推广、农田水利、土地资源的利用和农业经济制度与农村经济发展五个方面,对1999至2009年十年间西北近代农业经济发展的研究状况做一简单梳理。同时对研究中存在的不平衡性突出、研究深度不够、视野略显狭窄等问题进行了总结并简单阐述了几点建议。希望对深化西北近代农业经济发展的研究有所助益。     

Women and men as conservers, users and managers of agrobiodiversity: A feminist social–ecological approach

This feminist socio-ecological heuristic studies agrobiodiversity management, conservation and utilisation by men and women. Four approaches are characterised for an analysis of institutions, local knowledge, household economics and commodity chains. They are in turn linked to unveil their potential contributions towards a better understanding women's handling of, interests in and dependence on agrobiodiversity. The conceptualisation of a feminist framework aims at better informed research into the relationships between women, men and plants and is illustrated by examples from South Asia. Insights derived from a feminist socio-ecological perspective on agricultural biodiversity serve to generate gender-relevant policy recommendations towards establishing sustainable farming systems.     

公允价值变动会影响审计收费吗?——基于沪市A股上市公司的经验研究

公允价值计量模式在提高会计信息相关性的同时,如何保证其可靠性是理论和实务界关注的焦点。外部审计对保障会计信息可靠性发挥着至关重要的作用,而审计收费在一定程度上反映了审计师的谨慎程度和审计力度。文章研究了2008—2010年沪市A股上市公司公允价值变动损益对审计收费的影响,发现公允价值变动损益绝对值越大,审计收费越高;公允价值变动损益的正负值对审计收费产生影响,公允价值变动损益为正值时其对审计收费的影响更加显著;由十大会计师事务所审计的公司,其公允价值变动损益对审计收费的影响显著高于非十大事务所审计的公司。     

Child development and family resources: Evidence from the second generation of the 1958 British birth cohort

Studies of American and recently British children suggest that there is a link between family income and child development, in particular that one consequence of child poverty is to hold back cognitive development. This paper investigates the impact of family income, material deprivation, maternal education and child-rearing behaviour on an indicator of cognitive functioning, using British data on children aged 6 to 17 whose mothers are members of the 1958 Birth Cohort Study. The poorer average cognitive functioning among children from the lowest income groups could largely be accounted for, statistically, by the greater material disadvantage of these groups. These analyses provide evidence to suggest that low income has detrimental effects on children's cognitive functioning through the operation of longer-term material disadvantage, and that these effects may be mitigated by positive parental behaviours. Received: 31 July 1999/Accepted: 26 September 2000     

Skill premium effects on fertility and female labor force supply

In the last twenty years the United States has seen a positive relationship between female labor supply and total fertility rates, which differs from the pattern observed over the preceding years. We construct a general equilibrium overlapping generations model capable of generating this changing relationship between fertility and female labor supply. We argue that skilled biased technological change in recent decades has increased the skill premium and has therefore decreased the relative cost of (unskilled) child care services. The positive effect of the increase in female mean wages on fertility rates, and the inducement for labor force participation provided by the reduction in the relative cost of child care services, generated the positive relationship between fertility rates and female labor force participation in the last two decades.All correspondence to Amaia Iza. This paper has benefited particularly from comments by Juan Carlos Conesa, Pedro Mira and Sara de la Rica. We also acknowledge countless conversations with María Paz Espinosa and useful comments from Jaime Alonso, Jose María Da-Rocha, Tim Kehoe, José Victor Rios-Rull, when the paper was presented at the IV Workshop of Dynamic Macroeconomics held in Vigo (Spain, July 1999) and in Universidade de Vigo (October, 2000). We also thank an anonymous referee whose comments enabled us to improve the paper considerably. Financial support from Universidad del País Vasco 9UPV 00035.321-13511/2001, Ministerio de Educación y Ciencia PB097-0620, MCYT BEC2000/1394 and Instituto de la mujer (Ministerio de Trabajo y Asuntos Sociales) MTAS 33/00 is gratefully acknowledged. Any remaining errors are the authors responsibility. Responsible editor: Junsen Zhang.     

International trade, foreign direct investment, and transaction costs in languages

We examine the roles of major trade languages in international trade and foreign direct investment (FDI) flows. Empirical results confirm that speaking a common language increases trade and FDI flows, yet the effect of major languages is more substantial in FDI than in international trade. In addition, we find evidence of a hierarchy in transaction costs of major languages in both trade and FDI.     

Bayesian variable selection with strong heredity constraints

In this paper, we propose a Bayesian variable selection method for linear regression models with high-order interactions. Our method automatically enforces the heredity constraint, that is, a higher order interaction term can exist in the model only if both of its parent terms are in the model. Based on the stochastic search variable selection George and McCulloch (1993), we propose a novel hierarchical prior that fully considers the heredity constraint and controls the degree of sparsity simultaneously. We develop a Markov chain Monte Carlo (MCMC) algorithm to explore the model space efficiently while accounting for the heredity constraint by modifying the shotgun stochastic search algorithm Hans et al. (2007). The performance of the new model is demonstrated through comparisons with other methods. Numerical studies on both real data analysis and simulations show that our new method tends to find relevant variable more effectively when higher order interaction terms are considered.     

Generalized estimating equations by considering additive terms for analyzing time-course gene sets data

Time-course gene sets are collections of predefined groups of genes in some patients gathered over time. The analysis of time-course gene sets for testing gene sets which vary significantly over time is an important context in genomic data analysis. In this paper, the method of generalized estimating equations (GEEs), which is a semi-parametric approach, is applied to time-course gene set data. We propose a special structure of working correlation matrix to handle the association among repeated measurements of each patient over time. Also, the proposed working correlation matrix permits estimation of the effects of the same gene among different patients. The proposed approach is applied to an HIV therapeutic vaccine trial (DALIA-1 trial). This data set has two phases: pre-ATI and post-ATI which depend on a vaccination period. Using multiple testing, the significant gene sets in the pre-ATI phase are detected and data on two randomly selected gene sets in the post-ATI phase are also analyzed. Some simulation studies are performed to illustrate the proposed approaches. The results of the simulation studies confirm the good performance of our proposed approach.