Model averaging has long been proposed as a powerful alternative to model selection in regression analysis. However, how well it performs in high-dimensional regression is still poorly understood.

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 78, No. 5 (NOVEMBER 2016), pp. 1079-1102 (24 pages) We consider testing regression coefficients in high dimensional ...

Researchers have explained how large language models like GPT-3 are able to learn new tasks without updating their parameters, despite not being trained to perform those tasks. They found that these ...

Microbiome sequencing data are known to be biased; the measured taxa relative abundances can be systematically distorted from their true values at every step in the experimental/analysis workflow. If ...

In a thermally linear system, superposition may be applied to predict the transient response of the system to step changes in input power. This article is the second in a two-part series. Part one of ...

Citations: Blattberg, Robert. 1981. An Assessment of the Contribution of Log Linear Models to Marketing Research. Journal of Marketing. (2)89-97.

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision ...

Modeling a railroad is hard. Railroads are large, linear pieces of civil engineering. So many modelers are drawn to the smallest scale they can use. Recently a new scale, named T, at 1:450 has been ...