Works (16)

Updated: April 3rd, 2024 20:49

2024 journal article

WEAK SIGNAL INCLUSION UNDER DEPENDENCE AND APPLICATIONS IN GENOME-WIDE ASSOCIATION STUDY

ANNALS OF APPLIED STATISTICS, 18(1), 841–857.

By: X. Jeng n, Y. Hu n, Q. Sun* & Y. Li*

author keywords: Arbitrary covariance dependence; false negative control; underpowered GWAS; high- dimensional data; user-adaptive method
TL;DR: The challenge of retaining true signals that are not strong enough to be individually separable from a large amount of noise is addressed and false negative control (FNC) screening is presented, a data-driven method to efficiently regulate false negative proportion at a user-specified level. (via Semantic Scholar)
Source: Web Of Science
Added: March 25, 2024

2023 journal article

Estimating the proportion of signal variables under arbitrary covariance dependence

ELECTRONIC JOURNAL OF STATISTICS, 17(1), 950–979.

By: X. Jeng n

author keywords: Dependence adaptivity; high-dimension data; lower bound estimator; sparse signal
TL;DR: This paper defines mean absolute correlation (MAC) to measure the overall dependence strength and investigates a family of estimators for their performances in the full range of MAC and proposes a new estimator to better adapt to arbitrary covariance dependence. (via Semantic Scholar)
Source: Web Of Science
Added: November 20, 2023

2023 journal article

Transfer learning with false negative control improves polygenic risk prediction

PLOS GENETICS, 19(11).

By: X. Jeng n, Y. Hu n, V. Venkat n, T. Lu* & J. Tzeng n

Source: Web Of Science
Added: March 4, 2024

2021 journal article

Model Selection With Mixed Variables on the Lasso Path

SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, 83(1), 170–184.

By: X. Jeng n, H. Peng n & W. Lu n

author keywords: Jayanta K; Ghosh; Large p small n; Penalized regression; Variable selection; Primary; Secondary
TL;DR: A new variable selection procedure is developed to control over-selection of the noise variables ranking after the last relevant variable, and, at the same time, retain a high proportion of relevant variables ranking before the first noise variable. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: May 18, 2020

2020 journal article

Effective SNP ranking improves the performance of eQTL mapping

GENETIC EPIDEMIOLOGY, 44(6), 611–619.

By: X. Jeng n, J. Rhyne n, T. Zhang n & J. Tzeng n

author keywords: HC ranking; hotspot; multivariate response; penalized regression; trans-eQTL
MeSH headings : Computer Simulation; Data Analysis; Gene Expression Regulation; Genome-Wide Association Study; Humans; Models, Genetic; Polymorphism, Single Nucleotide / genetics; Quantitative Trait Loci / genetics
TL;DR: This paper illustrates how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eZTL mapping. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: April 14, 2020

2020 journal article

FastLORS: Joint modelling for expression quantitative trait loci mapping in R

STAT, 9(1).

By: J. Rhyne n, X. Jeng n, E. Chi & J. Tzeng n

author keywords: block coordinate descent; eQTL mapping; low-rank approximation; proximal gradient descent; sparse regression
TL;DR: FastLORS is a software package that implements a new algorithm to solve sparse multivariate regression for expression quantitative trait loci (eQTLs) mapping that reduces the computational cost compared with LORS. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID
Added: May 7, 2020

2019 journal article

Predictor ranking and false discovery proportion control in high-dimensional regression

JOURNAL OF MULTIVARIATE ANALYSIS, 171, 163–175.

By: X. Jeng n & X. Chen*

TL;DR: The procedure can consistently estimate the FDP of variable selection as long as the de-sparsified Lasso estimator is asymptotically normal and compares favorably to existing methods in ranking efficiency and FDP control. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: April 22, 2019

2019 journal article

Variable selection via adaptive false negative control in linear regression

ELECTRONIC JOURNAL OF STATISTICS, 13(2), 5306–5333.

By: X. Jeng & X. Chen

author keywords: debiased Lasso; FNC-Reg; post-selection inference; variable screening
TL;DR: This paper proposes to directly estimate the false negative proportion (FNP) of a decision rule and select the smallest subset of predictors that has the estimated FNP less than a user-specified control level. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: January 27, 2020

2018 journal article

Efficient Signal Inclusion With Genomic Applications

Journal of the American Statistical Association, 1–23.

By: X. Jeng n, T. Zhang n & J. Tzeng n

author keywords: Dimension reduction; False-negative control; False-positive control; Ultrahigh dimension; Variable screening
TL;DR: The signal missing rate (SMR) is proposed as a new measure for false-negative control to account for the variability offalse-negative proportion and novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. (via Semantic Scholar)
Source: Crossref
Added: February 21, 2020

2018 journal article

High-dimensional inference for personalized treatment decision

ELECTRONIC JOURNAL OF STATISTICS, 12(1), 2074–2089.

By: X. Jeng n, W. Lu n & H. Peng n

author keywords: Large p small n; model misspecification; optimal treatment regime; robust regression
TL;DR: An asymptotically unbiased estimator based on Lasso solution for the interaction coefficients is proposed and the limiting distribution of the estimator is derived when baseline function of the regression model is unknown and possibly misspecified. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2016 journal article

Detecting weak signals in high dimensions

JOURNAL OF MULTIVARIATE ANALYSIS, 147, 234–246.

By: X. Jeng n

author keywords: False negative control; Multiple testing; Variable screening; Variable selection; Trichotomous analysis
TL;DR: This paper seeks to facilitate the detection of weak signals by introducing a new approach based on false negative instead of false positive control, which shows in theory its efficiency and adaptivity to the unknown features of the data including signal intensity and sparsity. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2016 journal article

Rare variants association analysis in large-scale sequencing studies at the single locus level

PLoS Computational Biology, 12(6).

By: X. Jeng, Z. Daye, W. Lu & J. Tzeng

Source: NC State University Libraries
Added: August 6, 2018

2014 journal article

Censored rank independence screening for high-dimensional survival data

Biometrika, 101(4), 799–814.

By: R. Song n, W. Lu n, S. Ma* & X. Jeng n

author keywords: High-dimensional survival data; Rank independence screening; Sure screening property
TL;DR: Simulations and an analysis of real data demonstrate that the proposed method performs competitively on survival data sets of moderate size and high-dimensional predictors, even when these are contaminated. (via Semantic Scholar)
Sources: Web Of Science, ORCID, Crossref
Added: August 6, 2018

2014 journal article

Parametric modeling of whole-genome sequencing data for CNV identification

BIOSTATISTICS, 15(3), 427–441.

By: S. Vardhanabhuti*, X. Jeng n, Y. Wu* & H. Li*

author keywords: Natural exponential family; Sparse segment identification; Variance stabilization
MeSH headings : DNA Copy Number Variations / genetics; Genome, Human / genetics; Humans; Models, Statistical; Sequence Analysis, DNA / methods
TL;DR: This paper considers parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data, and proposes a unified approach of using a mean-matching variance stabilizing transformation. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2013 journal article

Simultaneous discovery of rare and common segment variants

BIOMETRIKA, 100(1), 157–172.

By: X. Jeng, T. Cai & H. Li

author keywords: DNA copy number variant; Information pooling; Population structural variant
TL;DR: A proportion adaptive segment selection procedure that automatically adjusts to the unknown proportions of the carriers of the segment variants that is applied to analyze neuroblastoma samples and identifies a large number of copy number variants that are missed by single-sample methods. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Shrinkage and model selection with correlated variables via weighted fusion

Computational Statistics & Data Analysis, 53(4), 1284–1298.

By: Z. Daye* & X. Jeng*

TL;DR: The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection and allows the selection of more than n variables in a motivated way when the number of predictors p is larger than thenumber of observations n. (via Semantic Scholar)
Source: Crossref
Added: August 28, 2020

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