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Performance period determination and bad definition for credit scorecard has been a mix of fortune for the typical data modeler.
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models.
A new test is proposed for the weak white noise null hypothesis. The test is based on an automatic choice of the order for a Box-Pierce or Hong test statistic.
Supersaturated design (SSD) has received much recent interest because of its potential in factor screening experiments. In this pa- per, we provide equivalent conditions for two columns to be fully ...
We consider the problem of online linear regression on indi- vidual sequences. The goal in this paper is for the forecaster to output sequential predictions which are, after T time rounds, almost as...
The celebrated de la Garza phenomenon states that for a polyno- mial regression model of degree p−1 any optimal design can be based on at most p design points. In a remarkable paper, Yang [Ann...
A general lower bound is developed for the minimax risk when estimating an arbitrary functional. The bound is based on testing two composite hypotheses and is shown to be effective in estimating th...
Brownian motion with known positive drift is sampled in stages until it crosses a positive boundary a. A family of multistage samplers that con- trol the expected overshoot over the boundary by vary...
The exploration-exploitation tradeoff is among the central challenges of reinforcement learning. A hypothetical exact Bayesian learner would provide the optimal solution, but is intractable in general...
We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are the noisy realizations of the sum of an (appproximately) lo...
The sequential detection of an abrupt and persistent change in the dynamics of an arbitrary continuous-path stochastic process is considered; the optimality of the cumulative sums (CUSUM) test is esta...
The problem of decentralized parameter estimation is considered for diffusion-type processes whose drift coefficients are linear with respect to the unknown parameter. This problem is motivated by app...
We study positive measures that are solutions to an abstract optimisation problem, which is a generalisation of a classical variational problem with a constraint on information of a Kullback-Leibler t...
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...
We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is appropriate for HD...

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