Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Lopuhaä, Hendrik P."'
Autor:
Lopuhaä, Hendrik Paul
We show that the limiting variance of a sequence of estimators for a structured covariance matrix has a general form that appears as the variance of a scaled projection of a random matrix that is of radial type and a similar result is obtained for th
Externí odkaz:
http://arxiv.org/abs/2407.01974
We provide a unified approach to S-estimation in balanced linear models with structured covariance matrices. Of main interest are S-estimators for linear mixed effects models, but our approach also includes S-estimators in several other standard mult
Externí odkaz:
http://arxiv.org/abs/2208.01939
Autor:
Lopuhaä, Hendrik Paul
We provide a unified approach to a method of estimation of the regression parameter in balanced linear models with a structured covariance matrix that combines a high breakdown point and bounded influence with high asymptotic efficiency at models wit
Externí odkaz:
http://arxiv.org/abs/2208.00715
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We investigate the asymptotic behavior of the $L_p$-distance between a monotone function on a compact interval and a smooth estimator of this function. Our main result is a central limit theorem for the $L_p$-error of smooth isotonic estimators obtai
Externí odkaz:
http://arxiv.org/abs/1805.12430
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We consider the process $\widehat\Lambda_n-\Lambda_n$, where $\Lambda_n$ is a cadlag step estimator for the primitive $\Lambda$ of a nonincreasing function $\lambda$ on $[0,1]$, and $\widehat\Lambda_n$ is the least concave majorant of $\Lambda_n$. We
Externí odkaz:
http://arxiv.org/abs/1706.05173
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We consider Grenander type estimators for a monotone function $\lambda:[0,1]\to\mathbb{R}$, obtained as the slope of a concave (convex) estimate of the primitive of $\lambda$. Our main result is a central limit theorem for the Hellinger loss, which a
Externí odkaz:
http://arxiv.org/abs/1612.06647
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We consider two isotonic smooth estimators for a monotone baseline hazard in the Cox model, a maximum smooth likelihood estimator and a Grenander-type estimator based on the smoothed Breslow estimator for the cumulative baseline hazard. We show that
Externí odkaz:
http://arxiv.org/abs/1611.01506
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We consider the smoothed maximum likelihood estimator and the smoothed Grenander-type estimator for a monotone baseline hazard rate $\lambda_0$ in the Cox model. We analyze their asymptotic behavior and show that they are asymptotically normal at rat
Externí odkaz:
http://arxiv.org/abs/1609.06617
Autor:
Lopuhaä, Hendrik P., Musta, Eni
We consider kernel smoothed Grenander-type estimators for a monotone hazard rate and a monotone density in the presence of randomly right censored data. We show that they converge at rate $n^{2/5}$ and that the limit distribution at a fixed point is
Externí odkaz:
http://arxiv.org/abs/1512.07445
For a joint model-based and design-based inference, we establish functional central limit theorems for the Horvitz-Thompson empirical process and the H\'ajek empirical process centered by their finite population mean as well as by their super-populat
Externí odkaz:
http://arxiv.org/abs/1509.09273