Estimating equilibrium models of sorting across locations

While there is growing interest in measuring the size and scope of local spillovers, it is well understood that such spillovers cannot be distinguished from unobservable local attributes using solely the observed location decisions of individuals or firms. We propose an empirical strategy for recovering estimates of spillovers in the presence of unobserved local attributes for a broadly applicable class of equilibrium sorting models. Our approach relies on an IV strategy derived from the internal logic of the sorting model itself. We show practically how the strategy is implemented, provide intuition for our instruments, discuss the role of effective choice-set variation in identifying the model, and carry-out a series of Monte Carlo simulations to demonstrate performance in small samples. © 2007 The Author(s). Journal compilation Royal Economic Society 2007.

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains. © Institute of Mathematical Statistics, 2010.

"Call for prices": Strategic implications of raising consumers' costs

Many consumer durable retailers often do not advertise their prices and instead ask consumers to call them for prices. It is easy to see that this practice increases the consumers' cost of learning the prices of products they are considering, yet firms commonly use such practices. Not advertising prices may reduce the firm's advertising costs, but the strategic effects of doing so are not clear. Our objective is to examine the strategic effects of this practice. In particular, how does making price discovery more difficult for consumers affect competing retailers' price, service decisions, and profits? We develop a model in which a manufacturer sells its product through a high-service retailer and a low-service retailer. Consumers can purchase the retail service at the high-end retailer and purchase the product at the competing low-end retailer. Therefore, the high-end retailer faces a free-riding problem. A retailer first chooses its optimal service levels. Then, it chooses its optimal price levels. Finally, a retailer decides whether to advertise its prices. The model results in four structures: (1) both retailers advertise prices, (2) only the low-service retailer advertises price, (3) only the high-service retailer advertises price, and (4) neither retailer advertises price. We find that when a retailer does not advertise its price and makes price discovery more difficult for consumers, the competition between the retailers is less intense. However, the retailer is forced to charge a lower price. In addition, if the competing retailer does advertise its prices, then the competing retailer enjoys higher profit margins. We identify conditions under which each of the above four structures is an equilibrium and show that a low-service retailer not advertising its price is a more likely outcome than a high-service retailer doing so. We then solve the manufacturer's problem and find that there are several instances when a retailer's advertising decisions are different from what the manufacturer would want. We describe the nature of this channel coordination problem and identify some solutions. © 2010 INFORMS.

Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.

Atherosclerosis: Viewing the problem from a different perspective including possible treatment options

This paper proposes that atherosclerosis is initiated by a signaling event that deposits calcium hydroxyapatite (Ca-HAP). This event is preceded by a loss of mechanical structure in the arterial wall. After Ca-HAP has been deposited, it is unlikely that it will be reabsorbed because the solubility product constant (K sp) is very small, and the large stores of Ca +2 and PO 4-3 in the bones oppose any attempts to dissolve Ca-HAP by decreasing the common ions. The hydroxide ion (OH -) of Ca-HAP can be displaced in nature by fluoride (F -) and carbonate (CO 3-2) ions, and it is proposed that anions associated with cholesterol ester hydrolysis and, in very small quantities, the enolate of 7-ketocholesterol could also displace the OH -of Ca-HAP, forming an ionic bond. The free energy of hydration of Ca-HAP at 310 K is most likely negative, and the ionic radii of the anions associated with the hydrolysis of cholesterol ester are compatible with the substitution. Furthermore, examination of the pathology of atherosclerotic lesions by Raman and NMR spectroscopy and confocal microscopy supports deposition of Ca-HAP associated with cholesterol. Investigating the affinity of intermediates of cholesterol hydrolysis for Ca-HAP compared to lipoproteins such as HDL, LDL, and VLDL using isothermic titration calorimetry could add proof of this concept and may lead to the development of a new class of medications targeted at the deposition of cholesterol within Ca-HAP. Treatment of acute ischemic events as a consequence of atherosclerosis with denitrogenation and oxygenation is discussed. © the author(s), publisher and licensee Libertas Academica Ltd.

Design of Scheduling Algorithms Using Game Theoretic Ideas

Scheduling a set of jobs over a collection of machines to optimize a certain quality-of-service measure is one of the most important research topics in both computer science theory and practice. In this thesis, we design algorithms that optimize {\em flow-time} (or delay) of jobs for scheduling problems that arise in a wide range of applications. We consider the classical model of unrelated machine scheduling and resolve several long standing open problems; we introduce new models that capture the novel algorithmic challenges in scheduling jobs in data centers or large clusters; we study the effect of selfish behavior in distributed and decentralized environments; we design algorithms that strive to balance the energy consumption and performance.

The technically interesting aspect of our work is the surprising connections we establish between approximation and online algorithms, economics, game theory, and queuing theory. It is the interplay of ideas from these different areas that lies at the heart of most of the algorithms presented in this thesis.

The main contributions of the thesis can be placed in one of the following categories.

1. Classical Unrelated Machine Scheduling: We give the first polygorithmic approximation algorithms for minimizing the average flow-time and minimizing the maximum flow-time in the offline setting. In the online and non-clairvoyant setting, we design the first non-clairvoyant algorithm for minimizing the weighted flow-time in the resource augmentation model. Our work introduces iterated rounding technique for the offline flow-time optimization, and gives the first framework to analyze non-clairvoyant algorithms for unrelated machines.

2. Polytope Scheduling Problem: To capture the multidimensional nature of the scheduling problems that arise in practice, we introduce Polytope Scheduling Problem (\psp). The \psp problem generalizes almost all classical scheduling models, and also captures hitherto unstudied scheduling problems such as routing multi-commodity flows, routing multicast (video-on-demand) trees, and multi-dimensional resource allocation. We design several competitive algorithms for the \psp problem and its variants for the objectives of minimizing the flow-time and completion time. Our work establishes many interesting connections between scheduling and market equilibrium concepts, fairness and non-clairvoyant scheduling, and queuing theoretic notion of stability and resource augmentation analysis.

3. Energy Efficient Scheduling: We give the first non-clairvoyant algorithm for minimizing the total flow-time + energy in the online and resource augmentation model for the most general setting of unrelated machines.

4. Selfish Scheduling: We study the effect of selfish behavior in scheduling and routing problems. We define a fairness index for scheduling policies called {\em bounded stretch}, and show that for the objective of minimizing the average (weighted) completion time, policies with small stretch lead to equilibrium outcomes with small price of anarchy. Our work gives the first linear/ convex programming duality based framework to bound the price of anarchy for general equilibrium concepts such as coarse correlated equilibrium.