Related papers: Comment: Fisher Lecture: Dimension Reduction in Re…
Regression has attracted immense interest lately due to its effectiveness in tasks like predicting values. And Regression is of widespread use in multiple fields such as Economics, Finance, Business, Biology and so on. While considerable…
In this note my personal point of view on the question brought up for a discussion at the Conference "Diffraction 2008" by Andre Martin has been presented.
We consider an enlarged dimension reduction space in functional inverse regression. Our operator and functional analysis based approach facilitates a compact and rigorous formulation of the functional inverse regression problem. It also…
Rejoinder: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]
Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data across domains. Dimensionality-reduction algorithms involve complex optimizations and the reduced dimensions computed by these algorithms…
Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression models with multivariate covariates. It has been extended…
Rejoinder: Conditional Growth Charts [math.ST/0702634]
A possible thermalization of Fisher information is suggested for certain situations.
We make several comments on "Note on the Analytical Solution of the Rabi Model" (arXiv:1210.4946).
Reply to the comment [arXiv:0904.2989] on "Self-Diffusion in 2D Dusty-Plasma Liquids: Numerical-Simulation Results" [arXiv:0812.0338]
Response to Comment by A. Bussmann-Holder (arXiv:0909.3603)
This is an addendum to the Reply Comment [Phys. Rev. Lett. 102, 139602 (2009), arXiv:0811.0518] to Comment [Phys. Rev. Lett. 102, 139601 (2009), arXiv:0810.4791] on Letter [Phys. Rev. Lett. 100, 116101 (2008), arXiv:0804.1898].
Comment on Classifier Technology and the Illusion of Progress--Credit Scoring [math.ST/0606441]
Recent progress concerning regularization of supersymmetric theories is reviewed. Dimensional reduction is reformulated in a mathematically consistent way, and an elegant and general method is presented that allows to study the…
Regression is one of the most fundamental statistical inference problems. A broad definition of regression problems is as estimation of the distribution of an outcome using a family of probability models indexed by covariates. Despite the…
Here we give further evidences to support our scaling relation described in our previous paper [cond-mat/0006459, Phys. Rev. Lett. Vol.85, pp.1238 (2000)].
Rejoinder to "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].
The paper is withdrawn by the author. This was an embryon of the book which has now been published with World Scientific under the title "Lectures on Quantum Chromodynamics". See http://www.wspc.com.sg/books/physics/4443.html, where the…
Comment on B.A. Bernevig, D. Giuliano, and R.B. Laughlin, Phys. Rev. Lett. 86, 3392 (2001).
Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303]