Model selection for correlated data with diverging number of parameters

Cho, Hyunkeun; Qu, Annie

doi:10.5705/ss.2011.058

Cited by 27 publications

(67 citation statements)

References 33 publications

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“…As n →∞, the size of nonzero parameters detectable by the procedure can approach zero, but at a slower rate than the tuning parameter. This condition is required for the derivation of the asymptotic properties of the proposed procedure, and has been assumed by many authors (e.g., Peng & Fan, 2004; Wang et al, 2009; Cho & Qu, 2013; Fan & Tang, 2013). In real-world biomedical research, there usually exists a fixed minimum clinically important effect size.…”

Section: Variable Selection With a Penalized Pseudo-partial Likelimentioning

confidence: 99%

Variable selection for case-cohort studies with failure time outcome

Ni¹,

Cai²,

Zeng³

2016

Biometrika

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Case-cohort designs are widely used in large cohort studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large, so an efficient variable selection method is necessary. In this paper, we study the properties of a variable selection procedure using the smoothly clipped absolute deviation penalty in a case-cohort design with a diverging number of parameters. We establish the consistency and asymptotic normality of the maximum penalized pseudo-partial-likelihood estimator, and show that the proposed variable selection method is consistent and has an asymptotic oracle property. Simulation studies compare the finite-sample performance of the procedure with tuning parameter selection methods based on the Akaike information criterion and the Bayesian information criterion. We make recommendations for use of the proposed procedures in case-cohort studies, and apply them to the Busselton Health Study.

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Section: Variable Selection With a Penalized Pseudo-partial Likelimentioning

confidence: 99%

Variable selection for case-cohort studies with failure time outcome

Ni¹,

Cai²,

Zeng³

2016

Biometrika

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“…In such cases, a transformation matrix H i can be applied for each subject to fit the pQIF model 26 . For each fully observed individual without missing data, H i is expressed as an m × m identity matrix for the i th subject, where m is the total repeated time point.…”

Section: Methodsmentioning

confidence: 99%

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Cao

Zhi

Huang

et al. 2017

Sci Rep

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Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

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“…For variable selection, a penalty can be added to the QIF, defined as:

normalPQIF false(\overset{θ}{true}, R; y, P_{λ} false) = N {\overline{g}}_{N}^{T} {\overline{C}}^{- 1} {\overline{g}}_{N} + N \sum_{j = 1}^{q} P_{λ} false(∣ θ_{j} ∣ false),

where

\overset{boldC}{true} = false(1 / N false) \sum_{i = 1}^{N} g_{i} g_{i}^{T}

is the sample covariance of

g_{i}

and

P_{λ}

is the penalty function. We follow Cho and Qu () by choosing the nonconvex SCAD penalty for

P_{λ}

and use a local quadratic approximation algorithm to carry out the estimation and variable selection. The PQIF leads to some very nice variable selection properties provided that q increases as N increases.…”

Section: Methodsmentioning

confidence: 99%

“…Several alternative criteria have since been proposed, these include: quasi‐likelihood information criterion (QIC, Pan, ) and its variants (Hin and Wang, ), generalized Mallow's C

_{p}

(GC

_{p}

, Cantoni, Flemming, and Ronchetti, ), and BIC using the quadratic inference function (QIF) approach (Wang and Qu, ). More recently, model selection criteria have been proposed using penalized GEEs (Wang, Zhou, and Qu, ) and penalized QIFs (Cho and Qu, ), where both methods use the smoothly clipped absolute deviation (SCAD) penalty.…”

Section: Introductionmentioning

confidence: 99%

Fast forward selection for generalized estimating equations with a large number of predictor variables

2013

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We propose a new variable selection criterion designed for use with forward selection algorithms; the score information criterion (SIC). The proposed criterion is based on score statistics which incorporate correlated response data. The main advantage of the SIC is that it is much faster to compute than existing model selection criteria when the number of predictor variables added to a model is large, this is because SIC can be computed for all candidate models without actually fitting them. A second advantage is that it incorporates the correlation between variables into its quasi-likelihood, leading to more desirable properties than competing selection criteria. Consistency and prediction properties are shown for the SIC. We conduct simulation studies to evaluate the selection and prediction performances, and compare these, as well as computational times, with some well-known variable selection criteria. We apply the SIC on a real data set collected on arthropods by considering variable selection on a large number of interactions terms consisting of species traits and environmental covariates.

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Model selection for correlated data with diverging number of parameters

Cited by 27 publications

References 33 publications

Variable selection for case-cohort studies with failure time outcome

Variable selection for case-cohort studies with failure time outcome

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Fast forward selection for generalized estimating equations with a large number of predictor variables

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