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As the design-manufacturing interface becomes increasingly complicated with IC technology scaling, the corresponding process variability poses great challenges for nanoscale analog/RF design. Design o...
This paper concerns the design of tapers for coupling power between uniform and slow-light periodic waveguides. New optimization methods are described for designing robust tapers, which not only perfo...
Simulation-based optimal design techniques are a convenient tool for solving a particular class of optimal design problems. The goal is to find the optimal configuration of factor settings with respec...
A new framework for optimal classifier design     Class Imbalance  One Class SVM  F-measure  Recall  Precision  Fraud Detection  Level Set Method       font style='font-size:12px;'> 2013/6/14
The use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still...
In the common linear regression model the problem of determining optimal designs for least squares estimation is considered in the case where the observations are correlated. A necessary condition for...
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the select...
We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator.
A new approach to adaptive design of clinical trials is proposed in a general multi- parameter exponential family setting, based on generalized likelihood ratio statistics and optimal sequential tes...
The LASSO is a variable subset selection procedure in statistical linear regression based on ℓ1 penalization of the least-squares operator. Its behavior crucially depends, both in practice and...
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimizatio...
On calibration of design weights      Calibration  Linear regression estimator  GREG  Estimation of total andvariance       font style='font-size:12px;'> 2010/3/18
In the present investigation, we build a bridge between the generalized regression (GREG) estimator due to Deville and Sarndal (1992) and the linear regression estimator due to Hansen, Hurwitz and M...
A Kronecker-product design and its reducible associate classes
Variance components admissible estimation from some unbalanced data: formulae for the nested design (1)。
Cluster Allocation Design Networks     Cluster allocation  Inuence diagrams  Causal inference  Identication of policy effects  DAGs       font style='font-size:12px;'> 2009/9/22
Design Networks expand this frame- work by including experimental design decision nodes (Madrigal and Smith 2004). They provide semantics to discuss how a design decision strategy (such as a clus- ...
In a nonparametric regession model with random design, where the regression function m is given by rn (x). = E (Y I X = x), estimation of the location 0 (mode) and size m(B) of a unique maximum of ...

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