A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

As Low Birth Weight Babies Grow, Can 'Good' Parents Buffer this Adverse Factor? A Research Note.

This research note combines two national Taiwanese datasets to investigate the relationship between low birth weight (LBW) babies, their family background and their future academic outcomes. We find that LBW is negatively correlated with the probability of such children attending university at the age of 18; however, when both parents are college or senior high school graduates, such negative effects may be partially offset. We also show that discrimination against daughters does occur, but only in those cases where the daughters were LBW babies. Moreover, high parental education (HPE) can only buffer the LBW shock among moderately-LBW children (as compared to very-LBW children) and full term-LBW children (as compared to preterm-LBW children).

Nonlinear Mean Reversion and Arbitrage in the Gold Futures Market

Previous literatures take transaction costs as being negligible when analyzing the futures basis behavior in linear dynamic framework. However, we argue that the relationship between the futures and spot prices with the conventional linear cointegration approach may not be appropriate after taking transaction costs into account. In this paper, an incorporation of transaction costs presented by Dumas (1992) and Michael (1997) into the exponential smooth transition autoregressive (ESTAR) model developed by Granger and Terasvita (1993) is motivated to examine the dynamic relationship between daily gold futures and spot prices and the nonlinear behavior of the gold futures basis. Transaction costs may lead to the existence of neutral band for futures market speculation within which profitable trading opportunities are impossible. Further, our results indicate that the ESTAR model provides higher forecasting power than the linear AR(1) model.