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pro vyhledávání: '"Doss, Charles R."'
We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we study inference for the counterfactual density functions and contrasts the
Externí odkaz:
http://arxiv.org/abs/2403.19917
The vast majority of literature on evaluating the significance of a treatment effect based on observational data has been confined to discrete treatments. These methods are not applicable to drawing inference for a continuous treatment, which arises
Externí odkaz:
http://arxiv.org/abs/2202.03369
We consider so-called univariate unlinked (sometimes ``decoupled,'' or ``shuffled'') regression when the unknown regression curve is monotone. In standard monotone regression, one observes a pair $(X,Y)$ where a response $Y$ is linked to a covariate
Externí odkaz:
http://arxiv.org/abs/2007.00830
Akademický článek
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Publikováno v:
Journal of Computational and Graphical Statistics (2021), 30(2), 612-621
We develop a new method to fit the multivariate response linear regression model that exploits a parametric link between the regression coefficient matrix and the error covariance matrix. Specifically, we assume that the correlations between entries
Externí odkaz:
http://arxiv.org/abs/1808.10558
Autor:
Doss, Charles R., Weng, Guangwei
We consider bandwidth matrix selection for kernel density estimators (KDEs) of density level sets in $\mathbb{R}^d$, $d \ge 2$. We also consider estimation of highest density regions, which differs from estimating level sets in that one specifies the
Externí odkaz:
http://arxiv.org/abs/1806.00731
Autor:
Doss, Charles R.
We propose a likelihood ratio statistic for forming hypothesis tests and confidence intervals for a nonparametrically estimated univariate regression function, based on the shape restriction of concavity (alternatively, convexity). Dealing with the l
Externí odkaz:
http://arxiv.org/abs/1805.09873
Autor:
Doss, Charles R., Wellner, Jon A.
We study nonparametric maximum likelihood estimation of a log-concave density function $f_0$ which is known to satisfy further constraints, where either (a) the mode $m$ of $f_0$ is known, or (b) $f_0$ is known to be symmetric about a fixed point $m$
Externí odkaz:
http://arxiv.org/abs/1611.10335
Autor:
Doss, Charles R., Wellner, Jon A.
We study a likelihood ratio test for the location of the mode of a log-concave density. Our test is based on comparison of the log-likelihoods corresponding to the unconstrained maximum likelihood estimator of a log-concave density and the constraine
Externí odkaz:
http://arxiv.org/abs/1611.10348
Autor:
Doss, Charles R.
We study bracketing covering numbers for spaces of bounded convex functions in the $L_p$ norms. Bracketing numbers are crucial quantities for understanding asymptotic behavior for many statistical nonparametric estimators. Bracketing number upper bou
Externí odkaz:
http://arxiv.org/abs/1506.00034