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Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i
Externí odkaz:
http://arxiv.org/abs/2410.04028
Autor:
Maturo, Fabrizio, Porreca, Annamaria
This paper introduces a novel supervised classification strategy that integrates functional data analysis (FDA) with tree-based methods, addressing the challenges of high-dimensional data and enhancing the classification performance of existing funct
Externí odkaz:
http://arxiv.org/abs/2408.13179
Autor:
Carere, Giuseppe, Lie, Han Cheng
In this work we solve, for given bounded operators $B,C$ and Hilbert--Schmidt operator $M$ acting on potentially infinite-dimensional separable Hilbert spaces, the reduced rank approximation problem, $\text{min}\{\Vert{M-BXC}\Vert_{HS}:\ \text{dim}\
Externí odkaz:
http://arxiv.org/abs/2408.05104
Autor:
Chen, Bobby, Chen, Siyu, Dowlatabadi, Jason, Hong, Yu Xuan, Iyer, Vinayak, Mantripragada, Uday, Narang, Rishabh, Pandey, Apoorv, Qin, Zijun, Sheikh, Abrar, Sun, Hongtao, Sun, Jiaqi, Walker, Matthew, Wei, Kaichen, Xu, Chen, Yang, Jingnan, Zhang, Allen T., Zhang, Guoqing
Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefin
Externí odkaz:
http://arxiv.org/abs/2407.19078
Autor:
Kennerberg, Philip, Wit, Ernst C.
We consider $k$ square integrable random variables $Y_1,...,Y_k$ and $k$ random (row) vectors of length $p$, $X_1,...,X_k$ such that $X_i(l)$ is square integrable for $1\le i\le k$ and $1\le l\le p$. No assumptions whatsoever are made of any relation
Externí odkaz:
http://arxiv.org/abs/2408.10218
Autor:
Davies, Laurie
The goal of this paper is to provide a theory linear regression based entirely on approximations. It will be argued that the standard linear regression model based theory whether frequentist or Bayesian has failed and that this failure is due to an '
Externí odkaz:
http://arxiv.org/abs/2402.09858
Autor:
Zuo, Yijun, Zuo, Hanwen
The least squares of depth trimmed (LST) residuals regression, proposed in Zuo and Zuo (2023) \cite{ZZ23}, serves as a robust alternative to the classic least squares (LS) regression as well as a strong competitor to the famous least trimmed squares
Externí odkaz:
http://arxiv.org/abs/2312.05077
This paper establishes the functional average as an important estimand for causal inference. The significance of the estimand lies in its robustness against traditional issues of confounding. We prove that this robustness holds even when the probabil
Externí odkaz:
http://arxiv.org/abs/2312.00219
Autor:
Nickelsen, Daniel, Bah, Bubacarr
In the field of equation learning, exhaustively considering all possible equations derived from a basis function dictionary is infeasible. Sparse regression and greedy algorithms have emerged as popular approaches to tackle this challenge. However, t
Externí odkaz:
http://arxiv.org/abs/2311.13265
Publikováno v:
Journal of Causal Inference, Vol 12, Iss 1, Pp 140-55 (2024)
This article establishes the functional average as an important estimand for causal inference. The significance of the estimand lies in its robustness against traditional issues of confounding. We prove that this robustness holds even when the probab
Externí odkaz:
https://doaj.org/article/93ddd46cceee4452a6baf6025d8fc14b
