Related papers: Comment: Fisher Lecture: Dimension Reduction in Re…
Rejoinder of ``Statistical analysis of an archeological find'' [arXiv:0804.0079]
These lecture notes were written for the course 18.657, High Dimensional Statistics at MIT. They build on a set of notes that was prepared at Princeton University in 2013-14 that was modified (and hopefully improved) over the years.
Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Comment: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]
Comment: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]
Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested…
A Comment on the paper "Conservative Quantum Computing" by M. Ozawa, Phys. Rev. Lett. 89, 057902 (2002). The author replies in Phys. Rev. Lett. 91, 089802 (2003).
Rejoinder to "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303]
Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely, Maria L. Rizzo [arXiv:1010.0297]
Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely, Maria L. Rizzo [arXiv:1010.0297]
This entry for the SIGSPATIAL Special July 2010 issue on Similarity Searching in Metric Spaces discusses the notion of intrinsic dimensionality of data in the context of similarity search.
Rejoinder of ``Objective Priors: An Introduction for Frequentists'' by M. Ghosh [arXiv:1108.2120]
We present a geometrical interpretation of linear regression based on vectors in n dimensions (n the number of data points). This is to be used as a didactic tool for teachers when presenting that topic.
I criticize the claim, made in a recent article [C. M. Bender and L. R. Mead, Eur. J. Phys. 20, 117 (1999)], that in order to obtain the correct cross section for the scattering from a two-dimensional delta-function potential one must…
A brief comment on the paper hep-th/0508051 (with the title mentioned above) by F. Nasseri.
This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats each regression coefficient in turn as the interest parameter,…
This chapter provides a hands-on tutorial on the important technique known as self-reducibility. Through a series of "Challenge Problems" that are theorems that the reader will---after being given definitions and tools---try to prove, the…
In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data…
This work contains a set of lectures on defect structures, mainly in models described by scalar fields in diverse dimensions.
This is an overview of various aspects of the 6-vertex model in statistical mechanics and related models.