Generalized Least-Squares Estimation of a Subvector of Parameters in Randomized Fractional Factorial Experiments

Lentner, M. M.

doi:10.1214/aoms/1177697506

Ann. Math. Statist.

1969

DOI: 10.1214/aoms/1177697506

|View full text |Cite

Generalized Least-Squares Estimation of a Subvector of Parameters in Randomized Fractional Factorial Experiments

M. M. Lentner¹

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2011

2016

Publication Types

Select...

Book5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 16 publications

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging

Kim¹,

Hwang²,

Adluru

et al. 2016

Computer Vision – ECCV 2016

View full text Add to dashboard Cite

Consider an experimental design of a neuroimaging study, where we need to obtain p measurements for each participant in a setting where p′ (< p) are cheaper and easier to acquire while the remaining (p – p′) are expensive. For example, the p′ measurements may include demographics, cognitive scores or routinely offered imaging scans while the (p – p′) measurements may correspond to more expensive types of brain image scans with a higher participant burden. In this scenario, it seems reasonable to seek an “adaptive” design for data acquisition so as to minimize the cost of the study without compromising statistical power. We show how this problem can be solved via harmonic analysis of a band-limited graph whose vertices correspond to participants and our goal is to fully recover a multi-variate signal on the nodes, given the full set of cheaper features and a partial set of more expensive measurements. This is accomplished using an adaptive query strategy derived from probing the properties of the graph in the frequency space. To demonstrate the benefits that this framework can provide, we present experimental evaluations on two independent neuroimaging studies and show that our proposed method can reliably recover the true signal with only partial observations directly yielding substantial financial savings.

show abstract

Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging

Kim¹,

Hwang²,

Adluru

et al. 2016

Computer Vision – ECCV 2016

View full text Add to dashboard Cite

show abstract

The First Problem of Algebraic Regression

Grafarend

Awange

2011

Linear and Nonlinear Models

View full text Add to dashboard Cite

The Fifth Problem of Probabilistic Regression

Grafarend

Awange

2011

Linear and Nonlinear Models

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Generalized Least-Squares Estimation of a Subvector of Parameters in Randomized Fractional Factorial Experiments

Cited by 16 publications

References 3 publications

Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging

Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging

The First Problem of Algebraic Regression

The Fifth Problem of Probabilistic Regression

Contact Info

Product

Resources

About