Weighted False Discovery Rate Control in Large-Scale Multiple Testing

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to genome-wide association study is discussed.Comment: Revise

Political and institutional determinants of the tax mix : an empirical investigation for OECD countries

Modern tax systems show a mix of direct and indirect taxes. However, it is difficult if not impossible to explain actual tax systems on the ba-sis of optimality conditions. Political and institutional factors are some-times argued to explain the presence of very complex tax structures. Wepropose various hypotheses that relate the tax structure to some political and institutional explanatory variables. The hypotheses are tested by ap-plying panel data analysis on a large sample of OECD countries for the period 1965 to 1995. We conclude that political and institutional vari-ables do not substantially influence the actual shape of the tax structure.

Decomposability and modularity of economic interactions

No abstract availabl

Political and institutional determinants of the tax mix : an empirical investigation for OECD countries

Modern tax systems show a mix of direct and indirect taxes. However, it is difficult if not impossible to explain actual tax systems on the ba-sis of optimality conditions. Political and institutional factors are some-times argued to explain the presence of very complex tax structures. Wepropose various hypotheses that relate the tax structure to some political and institutional explanatory variables. The hypotheses are tested by ap-plying panel data analysis on a large sample of OECD countries for the period 1965 to 1995. We conclude that political and institutional vari-ables do not substantially influence the actual shape of the tax structure.

Covariate-assisted ranking and screening for large-scale two-sample inference

Two-sample multiple testing has a wide range of applications. The conventionalpractice first reduces the original observations to a vector of p-values and then chooses a cutoffto adjust for multiplicity. However, this data reduction step could cause significant loss ofinformation and thus lead to suboptimal testing procedures.We introduce a new framework fortwo-sample multiple testing by incorporating a carefully constructed auxiliary variable in inferenceto improve the power. A data-driven multiple-testing procedure is developed by employinga covariate-assisted ranking and screening (CARS) approach that optimally combines the informationfrom both the primary and the auxiliary variables. The proposed CARS procedureis shown to be asymptotically valid and optimal for false discovery rate control. The procedureis implemented in the R package CARS. Numerical results confirm the effectiveness of CARSin false discovery rate control and show that it achieves substantial power gain over existingmethods. CARS is also illustrated through an application to the analysis of a satellite imagingdata set for supernova detection