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Stochastic Search for Semiparametric Linear Regression Models
Stochastic Search Semiparametric Linear Regression Models
2011/7/6
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987).
The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs.
Threshold estimation based on a p-value framework in dose-response and regression settings
estimation based p-value framework dose-response regression settings
2011/7/5
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 ...
On improved estimation in a conditionally Gaussian regression
Conditionally Gaussian regression model Improved estimation James–Stein procedure Non-Gaussian Ornstein–Uhlenbeck process
2011/6/20
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...
Adaptive and Optimal Online Linear Regression on L1-balls
online linear regression indi-vidual sequences Adaptive Optimal
2011/6/20
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...
Recursive bias estimation for multivariate regression smoothers
nonparametric regression;smoother;kernel;thin-plate splines;stopping rules
2011/6/17
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...
A two-stage hybrid procedure for estimating an inverse regression function
Two-stage estimator bootstrap adaptive design asymptotic properties
2011/6/17
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...
High-dimensional instrumental variables regression and confidence sets
Instrumental variables sparsity STIV estimator endogeneity high-dimensional regression conic programming optimal instruments hereroscedasticity confidence intervals non-Gaussian errors variable selection unknown variance sign consistency
2011/6/17
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...
Matrix Variate Logistic Regression Analysis
Asymptotic theory Logistic regression Matrix variate covariates Regularization Tensor object
2011/6/17
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...
Multivariate convex regression with adaptive partitioning
Nonparametric regression shape constraint convex regression treed linear model
2011/6/17
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, ...
Pointwise Adaptive M-estimation in Nonparametric Regression
adaptation Huber function Lepski's method M-estimation minimax estimation nonparametric regression robust estimation pointwise estimation
2011/6/17
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...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
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...
Using Logistic Regression to Analyze the Balance of a Game: The Case of StarCraft II
Balance of a Game Logistic Regression StarCraft II
2011/6/16
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...