Zobrazeno 1 - 10
of 272
pro vyhledávání: '"Tsai, Chih-Ling"'
Publikováno v:
Journal of Business & Economic Statistics, 2021
Advancements in data collection techniques and the heterogeneity of data resources can yield high percentages of missing observations on variables, such as block-wise missing data. Under missing-data scenarios, traditional methods such as the simple
Externí odkaz:
http://arxiv.org/abs/2205.07302
Publikováno v:
Journal of Business & Economic Statistics, 2021
Measuring heterogeneous influence across nodes in a network is critical in network analysis. This paper proposes an Inward and Outward Network Influence (IONI) model to assess nodal heterogeneity. Specifically, we allow for two types of influence par
Externí odkaz:
http://arxiv.org/abs/2205.07297
In this article, we propose the mutual influence regression model (MIR) to establish the relationship between the mutual influence matrix of actors and a set of similarity matrices induced by their associated attributes. This model is able to explain
Externí odkaz:
http://arxiv.org/abs/2205.07294
For estimating the large covariance matrix with a limited sample size, we propose the covariance model with general linear structure (CMGL) by employing the general link function to connect the covariance of the continuous response vector to a linear
Externí odkaz:
http://arxiv.org/abs/2205.07174
Akademický článek
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Publikováno v:
In Journal of Econometrics October 2022 230(2):318-338
Publikováno v:
Statistica Sinica, 2021 Jan 01. 31(4), 1727-1748.
Externí odkaz:
https://www.jstor.org/stable/27089314
Publikováno v:
Statistica Sinica, 28: 1561-1581, 2018
We propose a new sparse estimation method, termed MIC (Minimum approximated Information Criterion), for generalized linear models (GLM) in fixed dimensions. What is essentially involved in MIC is the approximation of the $\ell_0$-norm with a continuo
Externí odkaz:
http://arxiv.org/abs/1607.05169
Publikováno v:
The Annals of Statistics, 2019 Jun 01. 47(3), 1505-1535.
Externí odkaz:
https://www.jstor.org/stable/26730431
Publikováno v:
Annals of Statistics 2010, Vol. 38, No. 6, 3811-3836
In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select v
Externí odkaz:
http://arxiv.org/abs/1211.3509