Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.

about

Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.

Item

http://www.wikidata.org/entity/Q46214600

description

2017 nî lūn-bûn

@nan

2017年の論文

@ja

2017年学术文章

@wuu

2017年学术文章

@zh

2017年学术文章

@zh-cn

2017年学术文章

@zh-hans

2017年学术文章

@zh-my

2017年学术文章

@zh-sg

2017年學術文章

@yue

2017年學術文章

@zh-hant

name

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@en

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@nl

type

Item

label

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@en

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@nl

prefLabel

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@en

Collaborative-controlled LASSO ...... tors in high-dimensional data.

@nl

P1476

Collaborative-controlled LASSO ...... ators in high-dimensional data

@en

P2093

Jenny Häggström

http://www.w3.org/2001/XMLSchema#string

Jessica M Franklin

http://www.w3.org/2001/XMLSchema#string

Mark J van der Laan

http://www.w3.org/2001/XMLSchema#string

Richard Wyss

http://www.w3.org/2001/XMLSchema#string

Sebastian Schneeweiss

http://www.w3.org/2001/XMLSchema#string

P2860

Comparative safety of antidepressant agents for children and adolescents regarding suicidal acts

Regularization Paths for Generalized Linear Models via Coordinate Descent

Doubly robust estimation in missing data and causal inference models.

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data

Improving propensity score weighting using machine learning.

Higher potency statins and the risk of new diabetes: multicentre, observational study of administrative databases.

Effects of aggregation of drug and diagnostic codes on the performance of the high-dimensional propensity score algorithm: an empirical example.

Improving propensity score estimators' robustness to model misspecification using super learner.

Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records

P304

1044-1063

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1177/0962280217744588

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Cheng Ju

P577

2017-12-11T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29226777

http://www.w3.org/2001/XMLSchema#string

P818

1706.10029

http://www.w3.org/2001/XMLSchema#string

P921

collaboration

P932

6039292

http://www.w3.org/2001/XMLSchema#string