This paper considers a regression approach to estimating signal detection parameters for rating data. The methodology is based on the statistical modeling of ordinal data and requires only standard statistical software such as SAS (SAS/STAT User's Guide, 1999) for computation. The approach is more efficient than the current practice of extracting the parameter estimates with the use of speciali...

This document contains proofs and additional experiments for the paper " The Falling Factorial Basis and Its Statistical Applications ". In Section A, we provide proofs to the key technical results in the main paper. In Section B, we give some motivating arguments and additional experiments for the higher order KS test.

Uncertainty, robustness, and flexibility are three of the issues widely viewed as central to the field of complex engineered systems. A key challenge is to make the consideration of these issues more quantitative and theoretically sound. This paper presents a detailed technical analysis of one question inter-relating all three issues. How can we quantify the benefits of flexibility in the desig...

We study a novel spline-like basis, which we name the falling factorial basis, bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factorial functions are not actually splines, but are close enough to splines that they...

OBJECTIVE To examine the factor structure of the 144-item Chinese version of the Temperament and Character Inventory (TCI) and its association with age and gender in a large non-clinical population. METHOD We recruited 1966 non-clinical participants in China who completed the TCI Chinese version. They were randomly divided into two independent samples. One sample (n=983) was used for explorat...

Recent literature has proposed integrating observed noise variables into a factorial design structure as an alternative robust design strategy. Because noise factors are often difficult to control or randomize, observing their values may be useful. This research compares existing alternatives involving unreplicated fractional factorial designs and observable noise factors.