| Year |
Citation |
Score |
| 2018 |
Wang H, Zhu R, Ma P. Optimal Subsampling for Large Sample Logistic Regression. Journal of the American Statistical Association. 113: 829-844. PMID 30078922 DOI: 10.1080/01621459.2017.1292914 |
0.303 |
|
| 2017 |
Akay A, Di Domenico T, Suen KM, Nabih A, Parada GE, Larance M, Medhi R, Berkyurek AC, Zhang X, Wedeles CJ, Rudolph KLM, Engelhardt J, Hemberg M, Ma P, Lamond AI, et al. The Helicase Aquarius/EMB-4 Is Required to Overcome Intronic Barriers to Allow Nuclear RNAi Pathways to Heritably Silence Transcription. Developmental Cell. 42: 241-255.e6. PMID 28787591 DOI: 10.1016/J.Devcel.2017.07.002 |
0.337 |
|
| 2016 |
Sun X, Dalpiaz D, Wu D, S Liu J, Zhong W, Ma P. Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model. Bmc Bioinformatics. 17: 324. PMID 27565575 DOI: 10.1186/S12859-016-1180-9 |
0.699 |
|
| 2016 |
Helwig NE, Shorter KA, Ma P, Hsiao-Wecksler ET. Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data. Journal of Biomechanics. PMID 27553848 DOI: 10.1016/J.Jbiomech.2016.07.035 |
0.674 |
|
| 2016 |
Helwig NE, Ma P. Smoothing spline ANOVA for super-large samples: scalable computation via rounding parameters Statistics and Its Interface. 9: 433-444. DOI: 10.4310/Sii.2016.V9.N4.A3 |
0.648 |
|
| 2015 |
Helwig NE, Ma P. Fast and Stable Multiple Smoothing Parameter Selection in Smoothing Spline Analysis of Variance Models With Large Samples Journal of Computational and Graphical Statistics. 24: 715-732. DOI: 10.1080/10618600.2014.926819 |
0.677 |
|
| 2015 |
Helwig NE, Gao Y, Wang S, Ma P. Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance Spatial Statistics. 14: 491-504. DOI: 10.1016/J.Spasta.2015.09.002 |
0.658 |
|
| 2015 |
Ma P, Sun X. Leveraging for big data regression Wiley Interdisciplinary Reviews: Computational Statistics. 7: 70-76. DOI: 10.1002/Wics.1324 |
0.301 |
|
| 2013 |
Dalpiaz D, He X, Ma P. Bias Correction in RNA-Seq Short-Read Counts Using Penalized Regression Statistics in Biosciences. 5: 88-99. DOI: 10.1007/S12561-012-9057-6 |
0.696 |
|
| 2011 |
Tenorio L, Andersson F, De Hoop M, Ma P. Data analysis tools for uncertainty quantification of inverse problems Inverse Problems. 27. DOI: 10.1088/0266-5611/27/4/045001 |
0.315 |
|
| 2009 |
Zamdborg L, Ma P. Discovery of protein-DNA interactions by penalized multivariate regression. Nucleic Acids Research. 37: 5246-54. PMID 19578060 DOI: 10.1093/Nar/Gkp554 |
0.336 |
|
| 2009 |
Ma P, Zhong W, Liu JS. Identifying Differentially Expressed Genes in Time Course Microarray Data Statistics in Biosciences. 1: 144-159. DOI: 10.1007/S12561-009-9014-1 |
0.349 |
|
| 2008 |
Ma P, Zhong W. Penalized Clustering of Large-Scale Functional Data With Multiple Covariates Journal of the American Statistical Association. 103: 625-636. DOI: 10.1198/016214508000000247 |
0.327 |
|
| 2006 |
Ma P, Castillo-Davis CI, Zhong W, Liu JS. A data-driven clustering method for time course gene expression data. Nucleic Acids Research. 34: 1261-9. PMID 16510852 DOI: 10.1093/Nar/Gkl013 |
0.33 |
|
| 2005 |
Zhong W, Zeng P, Ma P, Liu JS, Zhu Y. RSIR: regularized sliced inverse regression for motif discovery. Bioinformatics (Oxford, England). 21: 4169-75. PMID 16166098 DOI: 10.1093/Bioinformatics/Bti680 |
0.346 |
|
| 2005 |
Gu C, Ma P. Optimal smoothing in nonparametric mixed-effect models Annals of Statistics. 33: 1357-1379. DOI: 10.1214/009053605000000110 |
0.339 |
|
| 2005 |
Gu C, Ma P. Generalized Nonparametric Mixed-Effect Models: Computation and Smoothing Parameter Selection Journal of Computational and Graphical Statistics. 14: 485-504. DOI: 10.1198/106186005X47651 |
0.326 |
|
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