230 research outputs found
Optimal and Myopic Information Acquisition
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
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
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
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
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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