Testlet response theory (TRT) is a measurement model that can capture local dependency in testlet-based tests. One of the purported advantages of TRT over the more commonly-used polytomous IRT approach to modeling testlet-based tests is that it allows for ad hoc testlet construction in a testlet-based computer adaptive test (CAT). The goal of this study was to investigate the merits of such a C...

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper we propose general-to-specific model selection procedures to overcome these limitations. After showing that single-equation ...

We propose new inference tools for forward stepwise regression, least angle regression, and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a general scheme to perform valid inference after any selection event that can be characterized as y falling into a polyhedral set. This framework allows us to derive conditional (post-selection) hypothesis tests at any ...

Professional experience, published evaluations and external quality assessment schemes clearly indicate that among all types of measurement procedures used in clinical biochemistry, those based on immunochemical principles are generally the most inaccurate. In addition, the magnitude of the systematic error in each of the various procedures differs widely, leading to an inability to directly co...

Given n noisy samples with p dimensions, where n ≪ p, we show that the multistep thresholding procedure can accurately estimate a sparse vector β ∈ R in a linear model, under the restricted eigenvalue conditions (Bickel-Ritov-Tsybakov 09). Thus our conditions for model selection consistency are considerably weaker than what has been achieved in previous works. More importantly, this method allo...

Hard-particle packings have served as useful starting points to study the structure of diverse systems such as liquids, living cells, granular media, glasses, and amorphous solids. Howard Reiss has played a major role in helping to illuminate our understanding of hard-particle systems, which still offer scientists many interesting conundrums. Jammed configurations of hard particles are of great...

Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares est...

The skin biopsy is a simple procedure that can assist with the diagnosis of cutaneous disorders. More errors are made from failing to biopsy promptly than from performing unnecessary biopsies. Properly performed, it may confirm a diagnosis, remove cosmetically unacceptable lesions, and provide definitive treatment for a number of skin conditions. Skin biopsies are unique because the lesion can ...

Fully sequential indifference-zone selection procedures have been proposed in the simulation literature to select the system with the best mean performance from a group of simulated systems. However, the existing sequential indifference-zone procedures allocate an equal number of samples to the two systems in comparison even if their variances are drastically different. In this paper we propose...

This paper provides an advanced tutorial on the construction of ranking-and-selection procedures for selecting the best simulated system. We emphasize procedures that provide a guaranteed probability of correct selection, and the key theoretical results that are used to derive them.