6 papers · 1 filter
High-dimensional linear regression inference via weak convergence
Kou Fujimori, Koji Tsukuda
We prove weak convergence in a separable Hilbert space for estimators of high-dimensional regression coefficients, which yields asymptotic normality and enables direct use of stand…
A test for counting sequences of integer-valued autoregressive models
Yuichi Goto, Kou Fujimori
The integer autoregressive (INAR) model is one of the most commonly used models in nonnegative integer-valued time series analysis and is a counterpart to the traditional autoregre…
Sparse principal component analysis for high-dimensional stationary time series
Kou Fujimori, Yuichi Goto, Yan Liu +1
We consider the sparse principal component analysis for high-dimensional stationary processes. The standard principal component analysis performs poorly when the dimension of the p…
Moment convergence of the generalized maximum composite likelihood estimators for determinantal point processes
Kou Fujimori, Sota Sakamoto, Yasutaka Shimizu
The maximum composite likelihood estimator for parametric models of determinantal point processes (DPPs) is discussed. Since the joint intensities of these point processes are give…
Cox's proportional hazards model with a high-dimensional and sparse regression parameter
Kou Fujimori
This paper deals with the proportional hazards model proposed by D. R. Cox in a high-dimensional and sparse setting for a regression parameter. To estimate the regression parameter…
The Dantzig selector for a linear model of diffusion processes
Kou Fujimori
In this paper, a linear model of diffusion processes with unknown drift and diagonal diffusion matrices is discussed. We will consider the estimation problems for unknown parameter…