The models used in small-area inference often involve unobservable random effects. While this can significantly improve the adaptivity and flexibility of a model, it also increases the variability of both point and interval estimators. If we could test for the existence of the random effects, and if the test were to show that they were unlikely to be present, then we would arguably not need to incorporate them into the model, and thus could significantly improve the precision of the methodology. In this paper we suggest an approach of this type.
This paper concerns wavelet regression using a block thresholding procedure. Block thresholding methods utilize neighboring wavelet coefficients information to increase estimation accuracy. We propose to construct a data-driven block thresholding procedure using the Smoothly Clipped Absolute Deviation (SCAD) penalty. A simulation study demonstrates competitive finite sample performance of the proposed estimator compared to existing methods. We also show that the proposed estimator achieves optimal convergence rates in Besov spaces.
In high dimensional regression problems regularization methods have been a popular choice to address variable selection and multicollinearity. In this paper we study bridge regression that adaptively selects the penalty order from data and produces flexible solutions in various settings. We implement bridge regression based on the local linear and quadratic approximations to circumvent the nonconvex optimization problem.
We consider the problem of obtaining locally D-optimal designs for factorial experiments with qualitative factors at two levels each with binary response. Our focus is primarily on the 22 experiment. In this paper, we derive analytic results for special cases and indicate how to handle the general case. The performance of the uniform design in examined and we show that this design is highly efficient in general. For the general 2k case we show that the uniform design has a maximin property.
We consider an experiment with two qualitative factors at 2 levels each and a binary response, that follows a generalized linear model. In Mandal, Yang and Majumdar (2010) we obtained basic results and characterizations of locally D-optimal designs for special cases. As locally optimal designs depend on the assumed parameter values, a critical issue is the sensitivity of the design to misspeciffication of these values. In this paper we study the sensitivity theoretically and by simulation, and show that the optimal designs are quite robust.
Dr. Kim Love-Myers is replacing Dr. Chen as Associate Director of the Statistical Consulting Center. Kim comes to us from the Warnell School of Forestry, and she brings with her significant consulting experience along with an outstanding vision for consulting center operations. We look forward to a cordial and mutually-productive working relationship. Welcome Kim!
Dr. Jien Chen, Associate Director of the Statistical Consulting Center, left the department and the university on July 1 to relocate to Virginia to be with her husband, Dr. Qin Wang, who is an Assistant Professor at Virginia Commonwealth University. The department and the SCC will miss her valuable contributions. We are very grateful for all that Jien has done here at UGA and wish her the very best with her relocation and future endeavors. Thank you Jien!
Jun Ye is working at Massachusetts General Hospital. His job duties are quite similar to those in his consulting job at UGA. He assists medical doctors in Nephrology with the statistical analysis sections of their papers.
Dipankar Bandyopadhyay is an assistant professor in Biostatistics in the Department of Biostatistics, Bioinformatics and Epidemiology at the Medical University of South Carolina, Charleston, SC.
Lewis Jordan currently works as a research staff member for Prof. Richard Daniels in the Warnell School of Forest Resources, UGA.