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A Starting Note: A Historical Perspective in Lasso

Yıl 2021, Cilt: 13 Sayı: 1, 1 - 3, 03.09.2021
https://doi.org/10.33818/ier.872633

Öz

we provide history of lasso, and see new ventures and talk about key concept of debiased lasso. Lasso provided a good fit through sparse regression but did not deliver standard errors. The debiased lasso delivers.

Kaynakça

  • Belloni, A. and D. Chen, and V. Chernozhukov, and C. Hansen (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80, 2369-2429.
  • Belloni, A. and V. Chernozhukov, and C. Hansen (2014). Inference on treatment effects after selection among high dimensional controls. Review of Economic Studies, 81, 608-650.
  • Callot, L. and M. Caner, and O. Onder, E. Ulasan (2021). A nodewise regression approach to estimating large portfolios. Journal of Business and Economic Statistics, Forthcoming.
  • Caner, M. (2009). Lasso-type GMM estimation. Econometric Theory, 25, 270-290.
  • Caner, M. and A.B. Kock (2018). Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative lasso. Journal of Econometrics, 203, 143-168.
  • Caner, M. and X. Han, and Y. Lee (2018). Adaptive elastic net GMM estimation with many invalid moment conditions: Simultaneous model and moment selection. Journal of Business and Economic Statistics, 36, 24-46.
  • Knight, K. and W. Fu (2000). Asymptotics for lasso-type estimators. Annals of Statistics, 28, 1356-1378.
  • Knight, K. (2008). Shrinkage estimation for nearly singular designs. Econometric Theory, 24, 323-337.
  • Leeb, H. and B. Potscher (2003). The finite sample distribution of post-model selection estimators and uniform versus nonuniform approximations. Econometric Theory 19, 100-142.
  • Leeb, H. and B. Potscher (2005). Model selection and inference: facts and fiction. Econometric Theory, 21, 21-59.
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 58,267-288.
  • Van de Geer, S. and P. Buhlmann, and Y. Ritov, and R. Dezeure (2014). On asymptotically optimal confidence regions and tests for high dimensional models. Annals of Statistics, 42, 1166-1202.
  • Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418-1429.
Yıl 2021, Cilt: 13 Sayı: 1, 1 - 3, 03.09.2021
https://doi.org/10.33818/ier.872633

Öz

Kaynakça

  • Belloni, A. and D. Chen, and V. Chernozhukov, and C. Hansen (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80, 2369-2429.
  • Belloni, A. and V. Chernozhukov, and C. Hansen (2014). Inference on treatment effects after selection among high dimensional controls. Review of Economic Studies, 81, 608-650.
  • Callot, L. and M. Caner, and O. Onder, E. Ulasan (2021). A nodewise regression approach to estimating large portfolios. Journal of Business and Economic Statistics, Forthcoming.
  • Caner, M. (2009). Lasso-type GMM estimation. Econometric Theory, 25, 270-290.
  • Caner, M. and A.B. Kock (2018). Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative lasso. Journal of Econometrics, 203, 143-168.
  • Caner, M. and X. Han, and Y. Lee (2018). Adaptive elastic net GMM estimation with many invalid moment conditions: Simultaneous model and moment selection. Journal of Business and Economic Statistics, 36, 24-46.
  • Knight, K. and W. Fu (2000). Asymptotics for lasso-type estimators. Annals of Statistics, 28, 1356-1378.
  • Knight, K. (2008). Shrinkage estimation for nearly singular designs. Econometric Theory, 24, 323-337.
  • Leeb, H. and B. Potscher (2003). The finite sample distribution of post-model selection estimators and uniform versus nonuniform approximations. Econometric Theory 19, 100-142.
  • Leeb, H. and B. Potscher (2005). Model selection and inference: facts and fiction. Econometric Theory, 21, 21-59.
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 58,267-288.
  • Van de Geer, S. and P. Buhlmann, and Y. Ritov, and R. Dezeure (2014). On asymptotically optimal confidence regions and tests for high dimensional models. Annals of Statistics, 42, 1166-1202.
  • Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418-1429.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Mehmet Caner

Yayımlanma Tarihi 3 Eylül 2021
Gönderilme Tarihi 12 Şubat 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 13 Sayı: 1

Kaynak Göster

APA Caner, M. (2021). A Starting Note: A Historical Perspective in Lasso. International Econometric Review, 13(1), 1-3. https://doi.org/10.33818/ier.872633
AMA Caner M. A Starting Note: A Historical Perspective in Lasso. IER. Eylül 2021;13(1):1-3. doi:10.33818/ier.872633
Chicago Caner, Mehmet. “A Starting Note: A Historical Perspective in Lasso”. International Econometric Review 13, sy. 1 (Eylül 2021): 1-3. https://doi.org/10.33818/ier.872633.
EndNote Caner M (01 Eylül 2021) A Starting Note: A Historical Perspective in Lasso. International Econometric Review 13 1 1–3.
IEEE M. Caner, “A Starting Note: A Historical Perspective in Lasso”, IER, c. 13, sy. 1, ss. 1–3, 2021, doi: 10.33818/ier.872633.
ISNAD Caner, Mehmet. “A Starting Note: A Historical Perspective in Lasso”. International Econometric Review 13/1 (Eylül 2021), 1-3. https://doi.org/10.33818/ier.872633.
JAMA Caner M. A Starting Note: A Historical Perspective in Lasso. IER. 2021;13:1–3.
MLA Caner, Mehmet. “A Starting Note: A Historical Perspective in Lasso”. International Econometric Review, c. 13, sy. 1, 2021, ss. 1-3, doi:10.33818/ier.872633.
Vancouver Caner M. A Starting Note: A Historical Perspective in Lasso. IER. 2021;13(1):1-3.