Results 71 to 80 of about 120 (106)
Penalized Discriminant Analysis
, 1995 Fisher's linear discriminant analysis (LDA) is a popular data-analytic tool for studying the relationship between a set of predictors and a categorical response. In this paper we describe a penalized version of LDA.
Andreas Buja +2 more
core
On convex least squares estimation when the truth is linear. [PDF]
Electron J Stat, 2016 Chen Y, Wellner JA.
europepmc +1 more source
, 1995 ralized cross validation (GCV), provided that matrix decompositions of size n \Theta n can be carried out, where n is the sample size. We review the randomized trace technique and the backfitting algorithm, and remark that they can be combined to solve ...
Zhen Luo, Grace Wahba
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Locus-specific contig assembly in highly-duplicated genomes, using the BAC-RF method. [PDF]
Nucleic Acids Res, 2000 Lin YR +8 more
europepmc +1 more source
Strong convergence in nonparametric regression with truncated dependent data
In this paper we derive rates of uniform strong convergence for the kernel estimator of the regression function in a left-truncation model. It is assumed that the lifetime observations with multivariate covariates form a stationary [alpha]-mixing ...
Liang, Han-Ying, Qi, Yongcheng, Li, Deli
core
Wavelet Regression For Random Uniform Design
, 1997 The current research on wavelet regression has been mostly focused on equispaced samples. In general nonequispaced samples require different treatment.
Lawrence D. Brown, T. Tony Cai
core
D-optimal designs for two-variable logistic regression model with restricted design space. [PDF]
Commun Stat Theory MethodsZhai Y, Wang C, Lin HY, Fang Z.
europepmc +1 more source
Nonparametric estimation of multivariate scale mixtures of uniform densities. [PDF]
J Multivar Anal, 2012 Pavlides MG, Wellner JA.
europepmc +1 more source
Multivariate Local Polynomial Regression For Time Series: Uniform Strong Consistency And Rates
, 1996 Local high-order polynomial fitting is employed for the estimation of the multivariate regression function m (x 1 , . . . , x d ) = E [y (Y d ) | X 1 = x 1 , . . . , X d = x d ], and of its partial derivatives, for stationary random processes {Y i , X i }
Elias Masry
core
Estimation of a discrete monotone distribution. [PDF]
Electron J Stat, 2009 Jankowski HK, Wellner JA.
europepmc +1 more source