Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published...

We conduct an experiment to explore the time-consistency of risk preferences in a multi-period betting game. Specifically, subjects planned their contingent betting decisions in advance then played the game dynamically later to determine whether their respective decisions matched. We find that...

Motivated by an analysis of U.S. house price index (HPI) data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling...

This article is concerned with feature screening and variable selection for varying coefficient models with ultrahigh-dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical...

Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for...

When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and...