7 citations · 12 across the 7 of their papers we have counts for
7 papers
Sure independence screening for covariate-dependent extreme value index estimation
Takuma Yoshida, Yuta Umezu
One of the main topics in extreme value analysis is the estimation of the extreme value index, which characterizes the tail behavior of a distribution. Although covariate dependent…
Selective Inference in Propensity Score Analysis
Yoshiyuki Ninomiya, Yuta Umezu, Ichiro Takeuchi
Selective inference (post-selection inference) is a methodology that has attracted much attention in recent years in the fields of statistics and machine learning. Naive inference…
Selective Inference via Marginal Screening for High Dimensional Classification
Yuta Umezu, Ichiro Takeuchi
Post-selection inference is a statistical technique for determining salient variables after model or variable selection. Recently, selective inference, a kind of post-selection inf…
Selective Inference for Change Point Detection in Multi-dimensional Sequences
Yuta Umezu, Ichiro Takeuchi
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be form…
Post Selection Inference with Kernels
Makoto Yamada, Yuta Umezu, Kenji Fukumizu +1
We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and m…
On the Consistency of the Bias Correction Term of the AIC for the Non-Concave Penalized Likelihood Method
Yuta Umezu, Yoshiyuki Ninomiya
Penalized likelihood methods with an -type penalty, such as the Bridge, the SCAD, and the MCP, allow us to estimate a parameter and to do variable selection, simultaneously…
AIC for Non-concave Penalized Likelihood Method
Yuta Umezu, Yusuke Shimizu, Hiroki Masuda +1
Non-concave penalized maximum likelihood methods, such as the Bridge, the SCAD, and the MCP, are widely used because they not only do parameter estimation and variable selection si…