Evaluating Causal Dominance of CTmeta-Analyzed Lagged Regression Estimates

Average dominance for polygenes: drawbacks of regression estimates.

T HE dominance of genes controlling fitness components and other quantitative characters is important for theoretical predictions concerning the maintenance of genetic variability (see e.&, CABALLERO and KEIGHTLEY 1994). Consider, for instance, mutation selection balance at a diallelic autosomal locus with mutation rate p to the deleterious allele and fitnesses 1, 1-sh, and 1-s, with s > 0 and ...

Estimation in functional lagged regression

The paper introduces a functional time series (lagged) regression model. The impulse response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric condition on the temporal dependence of the ...

Evaluating Alcoholics Anonymous's effect on drinking in Project MATCH using cross-lagged regression panel analysis.

OBJECTIVE The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. METHOD The method used is structural equation modeling of panel data with cross-lagged partial regressio...

An Ordered Lasso and Sparse Time-Lagged Regression

We consider a regression scenario where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of `1-regularized regression (lasso) for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main application of this idea is to time-lagged regression, where we predic...

The causal relationship between clinical activity and journal use in a hospital library as analyzed by multiple regression.