226 research outputs found

    Optimal and Myopic Information Acquisition

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    We consider the problem of optimal dynamic information acquisition from many correlated information sources. Each period, the decision-maker jointly takes an action and allocates a fixed number of observations across the available sources. His payoff depends on the actions taken and on an unknown state. In the canonical setting of jointly normal information sources, we show that the optimal dynamic information acquisition rule proceeds myopically after finitely many periods. If signals are acquired in large blocks each period, then the optimal rule turns out to be myopic from period 1. These results demonstrate the possibility of robust and "simple" optimal information acquisition, and simplify the analysis of dynamic information acquisition in a widely used informational environment

    Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates

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    We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handling model selection in cases where it is difficult to specify the likelihood function. We derive the quadratic inference function-based estimators for the linear coefficients and the nonparametric functions when the dimension of covariates diverges, and establish asymptotic normality for the linear coefficient estimators and the rates of convergence for the nonparametric functions estimators for both finite and high-dimensional cases. The proposed method and theoretical development are quite challenging since the numbers of linear covariates and nonlinear components both increase as the sample size increases. We also propose a doubly penalized procedure for variable selection which can simultaneously identify nonzero linear and nonparametric components, and which has an asymptotic oracle property. Extensive Monte Carlo studies have been conducted and show that the proposed procedure works effectively even with moderate sample sizes. A pharmacokinetics study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Data and Incentives

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    Many firms, such as banks and insurers, condition their level of service on a consumer's perceived "quality," for instance their creditworthiness. Increasingly, firms have access to consumer segmentations derived from auxiliary data on behavior, and can link outcomes across individuals in a segment for prediction. How does this practice affect consumer incentives to exert (socially-valuable) effort, e.g. to repay loans? We show that the impact of an identified linkage on behavior and welfare depends crucially on the structure of the linkage---namely, whether the linkage reflects quality (via correlations in types) or a shared circumstance (via common shocks to observed outcomes)

    How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories

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    We propose a new way to quantify the restrictiveness of an economic model, based on how well the model fits simulated, hypothetical data sets. The data sets are drawn at random from a distribution that satisfies some application-dependent content restrictions (such as that people prefer more money to less). Models that can fit almost all hypothetical data well are not restrictive. To illustrate our approach, we evaluate the restrictiveness of two widely-used behavioral models, Cumulative Prospect Theory and the Poisson Cognitive Hierarchy Model, and explain how restrictiveness reveals new insights about them

    Active inductor shunt peaking in high-speed VCSEL driver design

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    An all transistor active inductor shunt peaking structure has been used in a prototype of 8-Gbps high-speed VCSEL driver which is designed for the optical link in ATLAS liquid Argon calorimeter upgrade. The VCSEL driver is fabricated in a commercial 0.25-um Silicon-on-Sapphire (SoS) CMOS process for radiation tolerant purpose. The all transistor active inductor shunt peaking is used to overcome the bandwidth limitation from the CMOS process. The peaking structure has the same peaking effect as the passive one, but takes a small area, does not need linear resistors and can overcome the process variation by adjust the peaking strength via an external control. The design has been tapped out, and the prototype has been proofed by the preliminary electrical test results and bit error ratio test results. The driver achieves 8-Gbps data rate as simulated with the peaking. We present the all transistor active inductor shunt peaking structure, simulation and test results in this paper.Comment: 4 pages, 6 figures and 1 table, Submitted to 'Chinese Physics C
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