Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Takanashi, Kōsaku"'
The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. The primary appeal of this estimator is its theoretical property, where
Externí odkaz:
http://arxiv.org/abs/2409.06288
Autor:
Takanashi, Kosaku, McAlinn, Kenichiro
We discuss the relation between the statistical question of inadmissibility and the probabilistic question of transience. Brown (1971) proved the mathematical link between the admissibility of the mean of a Gaussian distribution and the recurrence of
Externí odkaz:
http://arxiv.org/abs/2310.17891
Autor:
Nakagawa, Takumi, Sanada, Yutaro, Waida, Hiroki, Zhang, Yuhui, Wada, Yuichiro, Takanashi, Kōsaku, Yamada, Tomonori, Kanamori, Takafumi
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstream tasks efficiently. However, recent works pay little attention to t
Externí odkaz:
http://arxiv.org/abs/2304.09552
The estimation of heterogeneous treatment effects in the potential outcome setting is biased when there exists model misspecification or unobserved confounding. As these biases are unobservable, what model to use when remains a critical open question
Externí odkaz:
http://arxiv.org/abs/2304.07726
Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model. Significant levels of model uncertainty -- arising from these characteristics -- cannot be resolved by model
Externí odkaz:
http://arxiv.org/abs/2203.05197
Autor:
McAlinn, Kenichiro, Takanashi, Kosaku
This paper studies the asymptotic convergence of computed dynamic models when the shock is unbounded. Most dynamic economic models lack a closed-form solution. As such, approximate solutions by numerical methods are utilized. Since the researcher can
Externí odkaz:
http://arxiv.org/abs/2103.06483
Autor:
Nakagawa, Takumi, Sanada, Yutaro, Waida, Hiroki, Zhang, Yuhui, Wada, Yuichiro, Takanashi, Kōsaku, Yamada, Tomonori, Kanamori, Takafumi
Publikováno v:
In Neural Networks January 2024 169:226-241
Autor:
McAlinn, Kenichiro, Takanashi, Kosaku
This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model average (MMA) estimator of Hansen (2007), but, unlike MMA, simultaneou
Externí odkaz:
http://arxiv.org/abs/1912.01194
Autor:
Takanashi, Kōsaku, McAlinn, Kenichiro
We discuss the finite sample theoretical properties of online predictions in non-stationary time series under model misspecification. To analyze the theoretical predictive properties of statistical methods under this setting, we first define the Kull
Externí odkaz:
http://arxiv.org/abs/1911.08662
Autor:
Takanashi, Kosaku
We study local asymptotic normality of M-estimates of convex minimization in an infinite dimensional parameter space. The objective function of M-estimates is not necessary differentiable and is possibly subject to convex constraints. In the above ci
Externí odkaz:
http://arxiv.org/abs/1704.02840