A new multiple testing method in the dependent case

The most popular multiple testing procedures are stepwise procedures based on $P$-values for individual test statistics. Included among these are the false discovery rate (FDR) controlling procedures of Benjamini--Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289--300] and their offsprings. Even for models that entail dependent data, $P$-values based on marginal distributions are used. Unlike such methods, the new method takes dependency into account at all stages. Furthermore, the $P$-value procedures often lack an intuitive convexity property, which is needed for admissibility. Still further, the new methodology is computationally feasible. If the number of tests is large and the proportion of true alternatives is less than say 25 percent, simulations demonstrate a clear preference for the new methodology. Applications are detailed for models such as testing treatments against control (or any intraclass correlation model), testing for change points and testing means when correlation is successive.Comment: Published in at http://dx.doi.org/10.1214/08-AOS616 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

DEA-Based Incentive Regimes in Health-Care Provision

A major challenge to legislators, insurance providers and municipalities will be how to manage the reimbursement of health-care on partially open markets under increasing fiscal pressure and an aging population. Although efficiency theoretically can be obtained by private solutions using fixed-payment schemes, the informational rents and production distortions may limit their implementation. The healthcare agency problem is characterized by (i) a complex multi-input multi-output technology, (ii) information uncertainty and asymmetry, and (iii) fuzzy social preferences. First, the technology, inherently nonlinear and with externalities between factors, yield parametric estimation difficult. However, the flexible production structure in Data Envelopment Analysis (DEA) offers a solution that allows for the gradual and successive refinement of potentially nonconvex technologies. Second, the information structure of healthcare suggests a context of considerable asymmetric information and considerable uncertainty about the underlying technology, but limited uncertainty or noise in the registration of the outcome. Again, we shall argue that the DEA dynamic yardsticks (Bogetoft, 1994, 1997, Agrell and Bogetoft, 2001) are suitable for such contexts. A third important characteristic of the health sector is the somewhat fuzzy social priorities and the numerous potential conflicts between the stakeholders in the health system. Social preferences are likely dynamic and contingent on the disclosed information. Similarly, there are several potential hidden action (moral hazard) and hidden information (adverse selection) conflicts between the different agents in the health system. The flexible and transparent response to preferential ambiguity is one of the strongest justifications for a DEA-approach. DEA yardstick regimes have been successfully implemented in other sectors (electricity distribution) and we present an operalization of the power-parameter p in an pseudo-competitive setting that both limits the informational rents and incites the truthful revelation of information. Recent work (Agrell and Bogetoft, 2002) on strategic implementation of DEA yardsticks is commented in the healthcare context, where social priorities change the tradeoff between the motivation and coordination functions of the yardstick. The paper is closed with policy recommendations and some areas of further work.Data Envelopment Analysis, regulation, health care systems, efficiency, Health Economics and Policy,

Learning in sender-receiver games

game theory;learning;testing

Essays on Labor and Risk

This dissertation presents three essays in labor economics and risk. Chapter 1 examines how past effort can impact current effort, such as when effort is reduced following an interruption. I present a series of real-effort incentivized experiments in which both piece rates and leisure options were manipulated and find effort displays significant stickiness, even in the absence of switching costs. I demonstrate that this intertemporal evidence is indicative of effort “momentum”, rather than on-the-job learning, reciprocity, or income targeting. When employing an instrumental variables (IV) approach, approximately 50\% of the effort increase persists for 5 minutes after incentives return to baseline. Thus if a worker suffers a complete interruption in productivity, it would take an average of 15 minutes to return to 90\% of prior work effort. I further demonstrate that advanced knowledge does not significantly reduce this productivity loss. Chapter 2 examines how risk preferences differ over goods and in-kind monetary rewards. I study an incentivized experiment in which subjects allocate bundles of either Amazon.com goods or Amazon.com gift credit (which must be spent immediately) across uncertain states. Under a standard model of perfect information of prices and goods available, I demonstrate risk preferences across these treatments would be identical. In practice, I uncover substantial differences in risk preferences across goods and in-kind monetary rewards. With additional treatments, I find no evidence that these differences are driven by price or product uncertainty. Chapter 3 is joint work with David Dillenberger, Daniel Gottlieb, and Pietro Ortoleva. We study preferences over lotteries that pay a specific prize at uncertain dates. Expected Utility with convex discounting implies that individuals prefer receiving x in a random date with mean t over receiving x in t days for sure. Our experiment rejects this prediction. It suggests a link between preferences for payments at certain dates and standard risk aversion. Epstein-Zin (1989) preferences accommodate such behavior, and fit the data better than a model with probability weighting

Modeling of combination therapy to support drug discovery in oncology

Mathematical models based on ordinary differential equations, with impulses, are used to describe tumor growth after different treatment combinations, including chemicals as well as radiation. The models are calibrated, using a nonlinear mixed-effects framework, based on time series data of tumor volume from animal experiments. Important features incorporated into the models include natural cell death, and short-term as well as long-term response to radiation treatment, with or without co-treatment with a radiosensitizing compound. Tumor Static Exposure, defined as the treatment combinations that yield stability of the trivial solution to the system model, is introduced as a prediction tool that can also be used to compare and optimize combination therapies. The Tumor Static Exposure concept is illustrated practically, using calibrated models and data from animal experiments, as well as theoretically, using a linear cell cycle model to describe cancer growth subject to treatment with an arbitrary number of anticancer compounds

Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs

Estimating Marginal Returns to Higher Education in the UK

A long-standing issue in the literature on education is whether marginal returns to education fall as education rises. If the population differs in its rate of return, a closely related question is whether marginal returns to higher education fall as a greater fraction of the population enrolls. This paper proposes a nonparametric method of estimating marginal treatment effects in heterogeneous populations, and applies it to this question, examining returns to higher education in the UK. The results indicate that marginal returns to higher education fall as the proportion of the population with higher education rises, consistent with the Becker Woytinsky Lecture hypothesis.

Statistical modeling and inference for complex-structured count data with applications in genomics and social science

2020 Spring.Includes bibliographical references.This dissertation describes models, estimation methods, and testing procedures for count data that build upon classic generalized linear models, including Gaussian, Poisson, and negative binomial regression. The methodological extensions proposed in this dissertation are motivated by complex structures for count data arising in three important classes of scientific problems, from both genomics and sociological contexts. Complexities include large scale, temporal dependence, zero-inflation and other mixture features, and group structure. The first class of problems involves count data that are collected from longitudinal RNA sequencing (RNA-seq) experiments, where the data consist of tens of thousands of short time series of counts, with replicate time series under treatment and under control. In order to determine if the time course differs between treatment and control, we consider two questions: 1) whether the treatment affects the geometric attributes of the temporal profiles and 2) whether any treatment effect varies over time. To answer the first question, we determine whether there has been a fundamental change in shape by modeling the transformed count data for genes at each time point using a Gaussian distribution, with the mean temporal profile generated by spline models, and introduce a measurement that quantifies the average minimum squared distance between the locations of peaks (or valleys) of each gene's temporal profile across experimental conditions. We then develop a testing framework based on a permutation procedure. Via simulation studies, we show that the proposed test achieves good power while controlling the false discovery rate. We also apply the test to data collected from a light physiology experiment on maize. To answer the second question, we model the time series of counts for each gene by a Gaussian-Negative Binomial model and introduce a new testing procedure that enjoys the optimality property of maximum average power. The test allows not only identification of traditional differentially expressed genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and expose its good performance via simulation studies. The procedure reveals interesting biological insights when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of a marine diatom. The second class of problems involves analyzing group-structured sRNA data that consist of independent replicates of counts for each sRNA across experimental conditions. Most existing methods—for both normalization and differential expression—are designed for non-group structured data. These methods may fail to provide correct normalization factors or fail to control FDR. They may lack power and may not be able to make inference on group effects. To address these challenges simultaneously, we introduce an inferential procedure using a group-based negative binomial model and a bootstrap testing method. This procedure not only provides a group-based normalization factor, but also enables group-based differential expression analysis. Our method shows good performance in both simulation studies and analysis of experimental data on roundworm. The last class of problems is motivated by the study of sensitive behaviors. These problems involve mixture-distributed count data that are collected by a quantitative randomized response technique (QRRT) which guarantees respondent anonymity. We propose a Poisson regression method based on maximum likelihood estimation computed via the EM algorithm. This method allows assessment of the importance of potential drivers of different quantities of non-compliant behavior. The method is illustrated with a case study examining potential drivers of non-compliance with hunting regulations in Sierra Leone