Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Dicker, Lee"'
Consensus planning is a method for coordinating decision making across complex systems and organizations, including complex supply chain optimization pipelines. It arises when large interdependent distributed agents (systems) share common resources a
Externí odkaz:
http://arxiv.org/abs/2408.16462
Publikováno v:
International Journal of Forecasting 2023, Hierarchical Forecasting Special Issue
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregati
Externí odkaz:
http://arxiv.org/abs/2110.13179
Publikováno v:
In International Journal of Forecasting April-June 2024 40(2):470-489
Autor:
Ma, Ruijun, Dicker, Lee H.
Linear mixed models (LMMs) are widely used for heritability estimation in genome-wide association studies (GWAS). In standard approaches to heritability estimation with LMMs, a genetic relationship matrix (GRM) must be specified. In GWAS, the GRM is
Externí odkaz:
http://arxiv.org/abs/1901.02936
We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard definition of regret is intractable for nonconvex models even in offl
Externí odkaz:
http://arxiv.org/abs/1811.05095
In stochastic optimization, the population risk is generally approximated by the empirical risk. However, in the large-scale setting, minimization of the empirical risk may be computationally restrictive. In this paper, we design an efficient algorit
Externí odkaz:
http://arxiv.org/abs/1611.06686
Autor:
Feng, Long, Dicker, Lee H.
Nonparametric maximum likelihood (NPML) for mixture models is a technique for estimating mixing distributions that has a long and rich history in statistics going back to the 1950s, and is closely related to empirical Bayes methods. Historically, NPM
Externí odkaz:
http://arxiv.org/abs/1606.02011
Regularization is an essential element of virtually all kernel methods for nonparametric regression problems. A critical factor in the effectiveness of a given kernel method is the type of regularization that is employed. This article compares and co
Externí odkaz:
http://arxiv.org/abs/1605.08839
Autor:
Dicker, Lee H.
Publikováno v:
Bernoulli 2016, Vol. 22, No. 1, 1-37
We study asymptotic minimax problems for estimating a $d$-dimensional regression parameter over spheres of growing dimension ($d\to \infty$). Assuming that the data follows a linear model with Gaussian predictors and errors, we show that ridge regres
Externí odkaz:
http://arxiv.org/abs/1601.03900
Autor:
Dicker, Lee H., Erdogdu, Murat A.
We derive convenient uniform concentration bounds and finite sample multivariate normal approximation results for quadratic forms, then describe some applications involving variance components estimation in linear random-effects models. Random-effect
Externí odkaz:
http://arxiv.org/abs/1509.04388