Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Kurisu, Daisuke"'
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular tool to achieve this. Causal inference has been developed mainly for
Externí odkaz:
http://arxiv.org/abs/2406.19604
We introduce a multivariate local-linear estimator for multivariate regression discontinuity designs in which treatment is assigned by crossing a boundary in the space of running variables. The dominant approach uses the Euclidean distance from a bou
Externí odkaz:
http://arxiv.org/abs/2402.08941
Autor:
Kurisu, Daisuke, Matsuda, Yasumasa
This paper develops a general asymptotic theory of series estimators for spatial data collected at irregularly spaced locations within a sampling region $R_n \subset \mathbb{R}^d$. We employ a stochastic sampling design that can flexibly generate irr
Externí odkaz:
http://arxiv.org/abs/2402.02773
Regression discontinuity design (RDD) is widely adopted for causal inference under intervention determined by a continuous variable. While one is interested in treatment effect heterogeneity by subgroups in many applications, RDD typically suffers fr
Externí odkaz:
http://arxiv.org/abs/2309.01404
Autor:
Kurisu, Daisuke, Matsuda, Yasumasa
This paper develops a general asymptotic theory of local polynomial (LP) regression for spatial data observed at irregularly spaced locations in a sampling region $R_n \subset \mathbb{R}^d$. We adopt a stochastic sampling design that can generate irr
Externí odkaz:
http://arxiv.org/abs/2211.13467
Autor:
Ishihara, Takuya, Kurisu, Daisuke
This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) rule proposed by Manski (2004), which assigns individuals to treatments
Externí odkaz:
http://arxiv.org/abs/2210.17063
In this paper, we develop a general theory for adaptive nonparametric estimation of the mean function of a non-stationary and nonlinear time series model using deep neural networks (DNNs). We first consider two types of DNN estimators, non-penalized
Externí odkaz:
http://arxiv.org/abs/2207.02546
Empirical Bayes small area estimation based on the well-known Fay-Herriot model may produce unreliable estimates when outlying areas exist. Existing robust methods against outliers or model misspecification are generally inefficient when the assumed
Externí odkaz:
http://arxiv.org/abs/2108.11551
Autor:
Kurisu, Daisuke
This study develops an asymptotic theory for estimating the time-varying characteristics of locally stationary functional time series (LSFTS). We investigate a kernel-based method to estimate the time-varying covariance operator and the time-varying
Externí odkaz:
http://arxiv.org/abs/2105.11873
Autor:
Kurisu, Daisuke
In this study, we develop an asymptotic theory of nonparametric regression for a locally stationary functional time series. First, we introduce the notion of a locally stationary functional time series (LSFTS) that takes values in a semi-metric space
Externí odkaz:
http://arxiv.org/abs/2105.07613