نتایج جستجو برای: additive covariate model

تعداد نتایج: 2160069  

2003
Stefan Lang Samson B. Adebayo Ludwig Fahrmeir Winfried J. Steiner

Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...

Journal: :iranian journal of applied animal science 2013
a. chegini a.a. shadparvar n. ghavi hossein-zadeh

the objective of the present study was to estimate genetic trends for lactation milk yield, persistency of milk yield, somatic cell count and interval between first and second calving in holstein dairy cows of iran. the dataset consisted of 210,625 test day and 25,883 first parity cows with milk yield recorded from july 2002 to september 2007 comprising 97 herds in iran. breeding values of anim...

Journal: :Journal of machine learning research : JMLR 2016
Ashley Petersen Noah Simon Daniela Witten

We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is ...

2004
Anna Törner

The primary objective of this work was to investigate and compare the use of the Cox proportional hazards model and Aalen’s additive model in analysing survival data. Survival data from a study of 52 patients with advanced breast cancer was investigated using the Cox proportional hazards model. The model was optimized by examining different aspects by use of appropriate residual plots. Covariat...

Journal: :Statistics and Computing 2015
Mathew W. McLean Giles Hooker David Ruppert

We propose a procedure for testing the linearity of a scalar-on-function regression relationship. To do so, we use the functional generalized additive model (FGAM), a recently developed extension of the functional linear model. For a functional covariate X(t), the FGAM models the mean response as the integral with respect to t of F{X(t), t} where F (·, ·) is an unknown bivariate function. The F...

Journal: :iranian journal of applied animal science 2015
a. ebadi tabrizi m. tahmoorespur a. nejati javaremi

random regression models (rrm) have become common for the analysis of longitudinal data or repeated records on individual over time. the goal of this paper was to explore the use of random regression models with orthogonal / legendre polynomials (rrl) to analyze new repeated measures called clutch size (cs) as a meristic trait for iranian native fowl. legendre polynomial functions of increasing...

Journal: :Genetics 2007
Charles R Farber Juan F Medrano

Previous speed congenic analysis has suggested that the expression of growth and obesity quantitative trait loci (QTL) on distal mouse chromosomes (MMU) 2 and 11, segregating between the CAST/EiJ (CAST) and C57BL/6J-hg/hg (HG) strains, is dependent on sex. To confirm, fine map, and further evaluate QTL x sex interactions, we constructed congenic by recipient F2 crosses for the HG.CAST-(D2Mit329...

Journal: :Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 2014
Mathew W McLean Giles Hooker Ana-Maria Staicu Fabian Scheipl David Ruppert

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number ...

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