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We use p-values to identify the threshold level at which a regression function takes off from its baseline value, a problem motivated by applications in toxicological and pharmacological dose-response...
In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk ...
The paper considers the problem of estimating a p ≥ 2 dimensional mean vector of a multivariate conditionally normal distribution under quadratic loss. The problem of this type arises when estimatin...
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...
This paper presents a practical and simple fully nonparametric multivariate smooth- ing procedure that adapts to the underlying smoothness of the true regression function. Our estimator is easily co...
We consider a two-stage procedure (TSP) for estimating an inverse regression function at a given point, where isotonic regression is used at stage one to obtain an initial estimate and a local linea...
We propose an instrumental variables method for estimation in linear models with endogenous regressors in the high-dimensional setting where the sample size n can be smaller than the number of possi...
Logistic regression has been widely applied in the field of biostatistics for a long time. It aims to model the conditional success probability of an event of interest as the logit function of a lin...
We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, ...
This paper deals with the nonparametric estimation in heteroscedastic regression Yi = f(Xi) + i; i = 1; : : : ; n, with incomplete information, i.e. each real random variable i has a density gi wh...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component...
Recently, the market size of online game has been increasing astonishingly fast, and so does the importance of good game design. In online games, usually a human user competes with others, so the fa...
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameteri...
In regression analysis one wants to estimate the regression function from a data. In this paper we consider the rate of convergence for the nearest neighbor estimator in case that the regression funct...
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or m...

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