Does philanthropy reduce inequality?

Wealthy individuals often voluntarily provide public goods that the poor also consume. We show that, rather than reducing it, such philanthropy may aggravate absolute inequality in welfare achievement, while leaving the change in relative inequality ambiguous. Additionally, philanthropic preferences may increase the effectiveness of policies to redistribute income, instead of weakening them. Our results thus suggest that philanthropy and direct redistribution may often be better viewed as complements, rather than substitutes, in the context of inequality reduction. In so doing, they also bring into question the general normative case for large tax deductions for charitable contributions.     

Lower bounds on Bayes risks for estimating a normal variance: With applications

Brown and Gajek (1990) gave useful lower bounds on Bayes risks, which improve on earlier bounds by various authors. Many of these use the information inequality. For estimating a normal variance using the invariant quadratic loss and any arbitrary prior on the reciprocal of the variance that is a mixture of Gamma distributions, we obtain lower bounds on Bayes risks that are different from Borovkov-Sakhanienko bounds. The main tool is convexity of appropriate functionals as opposed to the information inequality. The bounds are then applied to many specific examples, including the multi-Bayesian setup (Zidek and his coauthors). Subsequent use of moment theory and geometry gives a number of new results on efficiency of estimates which are linear in the sufficient statistic. These results complement earlier results of Donoho, Liu and MacGibbon (1990), Johnstone and MacGibbon (1992) and Vidakovic and DasGupta (1994) for the location case.     

A continuous approximation for evaluating reliability of complex systems under stress-strength model.

Evaluation of system reliability for complex systems based on Taylor's approximation becomes increasingly intractable. Taguchi's concept of random experimentation has been exploited by English et al (1996) for discretization of complex systems and determination of reliability values. We indicate a few demerits of discretization method and propose to retain the continuous character of the original problem by evaluating system reliability using a range approximation method. Our proposed method works better than discretization approach in all the three engineering problems considered for the purpose of demonstration.     

The spokane heart study: weibull regression and coronary artery disease

Coronary artery calcium is a marker of coronary artery disease and measures the progression of atherosclerosis. It is measured by electron beam computed tomography, and the measured amount of coronary artery calcium is highly skewed to the right and left censored. The distribution of coronary artery calcium appears to be Weibull. We propose a Weibull regression model and we analyze the data using these techniques. Our analysis is based on data from the Spokane Heart Study, which is a cohort of about a thousand subjects that are assessed every two years for coronary artery calcium and risk factors of coronary artery disease. The major focus of the heart study is to determine the natural history of atherosclerosis in its early phase, and we analyze the data as a cross-sectional study with 859 subjects. We would also like to highlight the use of Weibull regression techniques in situations like this, where we have extreme right skewed data. Our main emphasis will be on examining the effect of the traditional risk factors of age, gender, lipid profile (cholesterol and HDL), patient history of lipid abnormality, hypertension, and smoking, and other family history risks on coronary artery calcium. We found that the most important factors influencing the disease were age, sex, and patient history of smoking and lipid abnormality.     

Vyāghranomics in space and time: Estimating habitat threats for Bengal,Indochinese, Malayan and Sumatran tigers

The wild tiger population in tropical Asia has dropped from about 100,000 to 3500 in the last century, and the need to conserve tiger habitats poses a challenge for the Global Tiger Recovery Program. This paper develops and uses a high-resolution monthly forest clearing database for 74 tiger habitat areas in ten countries to investigate habitat threats for Bengal, Indochinese, Malayan and Sumatran tigers. The econometric model links forest habitat loss and forest clearing to profitability calculations that are affected by market expectations, environmental conditions and evolving patterns of settlement, among others. It uses new spatial panel estimation methods that allow for temporal and spatial autocorrelation. The econometric results emphasize the role of short-run market variables, including the exchange rate, real interest rate and prices of agricultural products in forest clearing, with considerable variation in the estimated timing for response and impact elasticities across countries. The results highlight a critical message for the conservation policy community: Changes in world agricultural-product markets and national financial policies have significant, measurable effects on tropical forest clearing, with variable time lags and degrees of responsiveness across countries. Measuring these effects and pinpointing areas at risk can provide valuable guidance for policymakers, conservation managers, and donor institutions.     

Comparing multiple treatments to both positive and negative controls

In the past, most comparison to control problems have dealt with comparing k test treatments to either positive or negative controls. Dasgupta et al. [2006. Using numerical methods to find the least favorable configuration when comparing k test treatments to both positive and negative controls. Journal of Statistical Computation and Simulation 76, 251–265] enumerate situations where it is imperative to compare several test treatments to both a negative as well as a positive control simultaneously. Specifically, the aim is to see if the test treatments are worse than the negative control, or if they are better than the positive control when the two controls are sufficiently apart. To find critical regions for this problem, one needs to find the least favorable configuration (LFC) under the composite null. In their paper, Dasgupta et al. [2006. Using numerical methods to find the least favorable configuration when comparing k test treatments to both positive and negative controls. Journal of Statistical Computation and Simulation 76, 251–265] came up with a numerical technique to find the LFC. In this paper we verify their result analytically. Via Monte Carlo simulation we compare the proposed method to the logical single step alternatives: Dunnett's [1955. A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association 50, 1096–1121] or the Bonferroni correction. The proposed method is superior in terms of both the Type I error and the marginal power.     

Inapproximability results for the lateral gene transfer problem

This paper concerns the Lateral Gene Transfer Problem. This minimization problem, defined by Hallett and Lagergren (2001), is that of finding the most parsimonious lateral gene transfer scenario for a given pair of gene and species trees. Our main results are the following:

FINANCIAL CONTAGION THROUGH CAPITAL CONNECTIONS: A MODEL OF THE ORIGIN AND SPREAD OF BANK PANICS

Financial contagion is modeled as an equilibrium phenomenon in a dynamic setting with incomplete information and multiple banks. The equilibrium probability of bank failure is uniquely determined. We explore how the cross‐holding of deposits motivated by imperfectly correlated regional liquidity shocks can lead to contagious effects conditional on the failure of a financial institution. We show that contagious bank failure occurs with positive probability in the unique equilibrium of the economy and demonstrate that the presence of such contagion risk can prevent banks from perfectly insuring each other against liquidity shocks via the cross‐holding of deposits. (JEL: G2, C7)     

Comparison to control in logistic regression

We are interested in comparing logistic regressions for several test treatments or populations with a logistic regression for a standard treatment or population. The research was motivated by some real life problems, which are discussed as data examples. We propose a step-down likelihood ratio method for declaring differences between the test treatments or populations and the standard treatment or population. Competitors based on the sequentially rejective Bonferroni Wald statistic, sequentially rejective exact Wald statistic and Reiers?l's statistic are also discussed. It is shown that the proposed method asymptotically controls the probability of type I error. A Monte Carlo simulation shows that the proposed method performs well for relatively small sample sizes, outperforming its competitors.