Least squares combinations (Granger & Ramanathan, 1984) are an important development in the forecast combination literature. However, ordinary least squares methods often perform poorly in real application due to the variability of coefficient/weight estimations. In this work, on one hand, we...

<Para ID="Par1">High dimensional data sets are now frequently encountered in many scientific fields. In order to select a sparse set of predictors that have predictive power and/or provide insightful understanding on which predictors really influence the response, a preliminary variable screening is typically...</para>

Given a dictionary of Mn initial estimates of the unknown true regression function, we aim to construct linearly aggregated estimators that target the best performance among all the linear combinations under a sparse q-norm (0 = q = 1) constraint on the linear coefficients. Besides identifying...

type="main" xml:id="rssb12043-abs-0001" <title type="main">Summary</title> <p>Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment...</p>

In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select a model, based on which forecasts are made. However, besides the well-known difficulty in dealing...

Given any countable collection of regression procedures (e.g., kernel, spline, wavelet, local polynomial, neural nets, etc.), we show that a single adaptive procedure can be constructed to share their advantages to a great extent in terms of global squared L2 risk. The combined procedure...