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2. Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics. (2016). Audrino, Francesco ; Knaus, Simon D.
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3. Testing the lag structure of assets’ realized volatility dynamics. (2015). Camponovo, Lorenzo ; Audrino, Francesco ; Roth, Constantin .
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4. Forecasting VARs, model selection, and shrinkage. (2015). Trenkler, Carsten ; Kascha, Christian.
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5. Nets: Network Estimation for Time Series. (2015). Brownlees, Christian ; Barigozzi, Matteo.
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6. Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models. (2013). Camponovo, Lorenzo ; Audrino, Francesco.
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7. Nets: Network estimation for time series. (2013). Brownlees, Christian ; Barigozzi, Matteo.
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8. Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models. (2013). Camponovo, Lorenzo ; Audrino, Francesco.
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9. Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics. (2012). Audrino, Francesco ; Knaus, Simon .
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1. Model-free sure screening via maximum correlation. (2016). Zhu, YU ; Huang, Qiming.
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2. Detecting weak signals in high dimensions. (2016). Jeng, Jessie X.
   In: Journal of Multivariate Analysis.
   RePEc:eee:jmvana:v:147:y:2016:i:c:p:234-246.

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3. Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression. (2016). Latouche, Pierre ; Mattei, Pierre-Alexandre ; Chiquet, Julien ; Bouveyron, Charles.
   In: Journal of Multivariate Analysis.
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4. Inference for biased models: A quasi-instrumental variable approach. (2016). Zhu, Lixing ; Gai, Yujie ; Lin, LU.
   In: Journal of Multivariate Analysis.
   RePEc:eee:jmvana:v:145:y:2016:i:c:p:22-36.

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5. Robust model-free feature screening via quantile correlation. (2016). Ma, Xuejun ; Zhang, Jingxiao.
   In: Journal of Multivariate Analysis.
   RePEc:eee:jmvana:v:143:y:2016:i:c:p:472-480.

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6. Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. (2016). Huang, Tao ; Ma, Shaohui ; Fildes, Robert.
   In: European Journal of Operational Research.
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7. Jackknife empirical likelihood test for high-dimensional regression coefficients. (2016). Zang, Yangguang ; Zhang, Sanguo ; Li, Qizhai.
   In: Computational Statistics & Data Analysis.
   RePEc:eee:csdana:v:94:y:2016:i:c:p:302-316.

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8. Adaptive conditional feature screening. (2016). Lin, LU ; Sun, Jing.
   In: Computational Statistics & Data Analysis.
   RePEc:eee:csdana:v:94:y:2016:i:c:p:287-301.

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10. Semiparametric Model Averaging of Ultra-High Dimensional Time Series. (2015). Li, Degui ; LINTON, OLIVER ; Chen, Jia ; Lu, Zudi.
    In: Discussion Papers.
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11. Variable selection after screening: with or without data splitting?. (2015). Yang, Yuhong ; Zhu, Xiaoyi.
    In: Computational Statistics.
    RePEc:spr:compst:v:30:y:2015:i:1:p:191-203.

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12. Sparse and efficient estimation for partial spline models with increasing dimension. (2015). Shang, Zuofeng ; Zhang, Hao.
    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:67:y:2015:i:1:p:93-127.

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13. Semiparametric model averaging of ultra-high dimensional time series. (2015). Li, Degui ; LINTON, OLIVER ; Chen, Jia ; Lu, Zudi.
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14. A lava attack on the recovery of sums of dense and sparse signals. (2015). Liao, Yuan ; Hansen, Christian ; Chernozhukov, Victor.
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15. Variable selection and estimation in high-dimensional models. (2015). Horowitz, Joel.
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16. A lava attack on the recovery of sums of dense and sparse signals. (2015). Liao, Yuan ; Hansen, Christian ; Chernozhukov, Victor.
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17. A semiparametric conditional capital asset pricing model. (2015). Ren, Yu ; CAI, ZONGWU ; Yang, Bingduo.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:61:y:2015:i:c:p:117-126.

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18. A flexible semiparametric forecasting model for time series. (2015). Li, Degui ; LINTON, OLIVER ; Lu, Zudi.
    In: Journal of Econometrics.
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19. Oracle inequalities for high dimensional vector autoregressions. (2015). Kock, Anders ; Callot, Laurent.
    In: Journal of Econometrics.
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20. Selection of tuning parameters in bridge regression models via Bayesian information criterion. (2014). Kawano, Shuichi.
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21. Regularized principal components of heritability. (2014). Fang, Yixin ; Feng, Yang ; Yuan, Ming.
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22. High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. (2014). Buhlmann, Peter ; Mandozzi, Jacopo .
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23. Tests for High Dimensional Generalized Linear Models. (2014). Chen, Song ; Guo, Bin.
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24. Analyzing Genome-Wide Association Studies with an FDR Controlling Modification of the Bayesian Information Criterion. (2014). Frommlet, Florian ; Bodenstorfer, Bernhard ; Dolejsi, Erich.
    In: PLOS ONE.
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25. Asymptotics of hierarchical clustering for growing dimension. (2014). Borysov, Petro ; Hannig, Jan ; Marron, J. S..
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:124:y:2014:i:c:p:465-479.

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26. Group subset selection for linear regression. (2014). Guo, YI ; Berman, Mark ; Gao, Junbin.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:75:y:2014:i:c:p:39-52.

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27. On selecting interacting features from high-dimensional data. (2014). Hall, Peter ; Xue, Jing-Hao.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:71:y:2014:i:c:p:694-708.

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28. A Comparative Study of the Lasso-type and Heuristic Model Selection Methods. (2013). Savin, Ivan.
    In: Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik).
    RePEc:jns:jbstat:v:233:y:2014:i:4:p:526-549.

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29. Pivotal estimation via square-root lasso in nonparametric regression. (2013). Chernozhukov, Victor ; Wang, Lie ; Belloni, Alexandre.
    In: CeMMAP working papers.
    RePEc:ifs:cemmap:62/13.

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30. Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood. (2013). Zhu, Shiwu ; Yao, Qiwei ; Wu, Billy.
    In: LSE Research Online Documents on Economics.
    RePEc:ehl:lserod:50043.

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31. Estimation in the presence of many nuisance parameters: Composite likelihood and plug-in likelihood. (2013). Zhu, Shiwu ; Yao, Qiwei ; Wu, Billy.
    In: Stochastic Processes and their Applications.
    RePEc:eee:spapps:v:123:y:2013:i:7:p:2877-2898.

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32. Variable selection in high-dimensional quantile varying coefficient models. (2013). Zhu, Zhongyi ; Tang, Yanlin ; Song, Xinyuan ; Wang, Huixia Judy.
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:122:y:2013:i:c:p:115-132.

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33. Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data. (2013). Cao, Longbing ; Wang, Cheng ; Miao, Baiqi.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:66:y:2013:i:c:p:140-149.

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34. Oracle Inequalities for Convex Loss Functions with Non-Linear Targets. (2013). Kock, Anders ; Caner, Mehmet.
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35. Lassoing the Determinants of Retirement. (2013). Kristensen, Johannes ; Kock, Anders ; Kallestrup-Lamb, Malene.
    In: CREATES Research Papers.
    RePEc:aah:create:2013-21.

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36. Oracle inequalities for high-dimensional panel data models. (2013). Kock, Anders.
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37. Variable selection in semiparametric regression analysis for longitudinal data. (2012). Zhao, Peixin ; Xue, Liugen.
    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:64:y:2012:i:1:p:213-231.

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38. Endogeneity in ultrahigh dimension. (2012). Liao, Yuan ; Fan, Jianqing.
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39. Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters. (2012). Zhu, Lixing ; Lin, LU.
    In: Journal of Multivariate Analysis.
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40. Principled sure independence screening for Cox models with ultra-high-dimensional covariates. (2012). Li, YI ; Zhao, Sihai Dave.
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:105:y:2012:i:1:p:397-411.

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41. Joint adaptive mean–variance regularization and variance stabilization of high dimensional data. (2012). Rao, Sunil J. ; Dazard, Jean-Eudes .
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:56:y:2012:i:7:p:2317-2333.

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42. Semiparametric regression models with additive nonparametric components and high dimensional parametric components. (2012). Du, Pang ; Liang, Hua.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:56:y:2012:i:6:p:2006-2017.

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43. On efficient calculations for Bayesian variable selection. (2012). Ruggieri, Eric ; Lawrence, Charles E..
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    RePEc:eee:csdana:v:56:y:2012:i:6:p:1319-1332.

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44. Focused estimation and model averaging with penalization methods: an overview. (2012). Claeskens, Gerda.
    In: Statistica Neerlandica.
    RePEc:bla:stanee:v:66:y:2012:i:3:p:272-287.

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45. Oracle Inequalities for High Dimensional Vector Autoregressions. (2012). Kock, Anders ; Callot, Laurent ; Laurent A. F. Callot, .
    In: CREATES Research Papers.
    RePEc:aah:create:2012-16.

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46. On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions. (2012). Kock, Anders.
    In: CREATES Research Papers.
    RePEc:aah:create:2012-05.

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47. Variable selection for varying coefficient models with measurement errors. (2011). Zhao, Peixin ; Xue, Liugen.
    In: Metrika: International Journal for Theoretical and Applied Statistics.
    RePEc:spr:metrik:v:74:y:2011:i:2:p:231-245.

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48. Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach. (2011). Song, Song.
    In: Papers.
    RePEc:arx:papers:1106.3921.

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49. The Dantzig selector in Coxs proportional hazards model. (2010). Fryzlewicz, Piotr ; Letue, Frederique ; Antoniadis, Anestis.
    In: LSE Research Online Documents on Economics.
    RePEc:ehl:lserod:30992.

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50. Independent rule in classification of multivariate binary data. (2009). Park, Jun Yong.
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:100:y:2009:i:10:p:2270-2286.

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