Results 181 to 190 of about 7,937,632 (296)

Lasso for hierarchical polynomial models

Canadian Journal of Statistics, EarlyView.
Abstract The divisibility conditions implicit in a polynomial hierarchy suggest parameter constraints in regression. With this idea, we establish strong and weak hierarchies for both the lasso and relaxed lasso. Our proposal extends prior work on hierarchical lasso, which was mainly concerned with models of degree 2.
H. Maruri‐Aguilar, S. Lunagómez
wiley   +1 more source

The Central Limit Theorem for th-Order Nonhomogeneous Markov Information Source

Journal of Probability and Statistics, 2013
Huilin Huang
doaj   +1 more source

On the structure of the sandpile identity element on Sierpiński gasket graphs. [PDF]

Comput Appl Math
Kaiser R, Sava-Huss E, Überbacher J.
europepmc   +1 more source

Sparse maximum likelihood estimation of regression models

Canadian Journal of Statistics, EarlyView.
Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley   +1 more source

Entropy Regularization in the Mean-Field Free-Energy Machine for Combinatorial Optimization. [PDF]

Entropy (Basel)
Gao W.
europepmc   +1 more source

Order restricted row‐column cluster randomized design

Canadian Journal of Statistics, EarlyView.
Abstract This research introduces a novel two‐stage cluster randomized design, the order restricted row‐column cluster randomized design. The new design builds upon the cluster randomized block design by incorporating a second layer of blocking, achieved through ranking cluster units that are randomly sampled from the population.
Dalton Hopper, Omer Ozturk, Olena Kravchuk   +2 more
wiley   +1 more source

Mitigating measurement error in misspecified small area models

Canadian Journal of Statistics, EarlyView.
Abstract In the framework of Small area estimation, we consider an area‐level model where a subset of covariates is measured with error. The extent of the error is assumed to be constant throughout the areas, and it is expressed by a scalar parameter γ$$ \gamma $$, which multiplies the deterministic covariance matrix of the estimator of the true ...
Diego Battagliese, Serena Arima, Brunero Liseo, Alessio Pollice   +3 more
wiley   +1 more source

Quantifying Dependence Between Random Vectors: A New Index with Applications. [PDF]

Entropy (Basel)
Zhang X, Yin C.
europepmc   +1 more source

Learning sparse mixture‐of‐experts generalized linear models in ultrahigh dimensions

Canadian Journal of Statistics, EarlyView.
Abstract Motivated by the challenges of heterogeneity and high dimensionality in the analysis of modern data, we investigate continuous regularization methods for learning sparse mixture‐of‐experts generalized linear models (MoE‐GLM). Although there are foundational results about regularized estimators in a broad class of regression models including ...
Pengqi Liu, Abbas Khalili
wiley   +1 more source

Expected value of the Sackin index under biased speciation models. [PDF]

J Math Biol
Bauman DJ, Rosenberg NA.
europepmc   +1 more source