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A Note on Penalized Spline Smoothing With Correlated Errors

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  1. Reiss Philip T. & Huang Lei & Mennes Maarten, 2010. "Fast Function-on-Scalar Regression with Penalized Basis Expansions," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-30, August.
  2. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
  3. Philip T. Reiss & Lei Huang & Pei‐Shien Wu & Huaihou Chen & Stan Colcombe, 2017. "Pointwise influence matrices for functional‐response regression," Biometrics, The International Biometric Society, vol. 73(4), pages 1092-1101, December.
  4. Dawid, H. & Harting, P. & Neugart, M., 2018. "Cohesion policy and inequality dynamics: Insights from a heterogeneous agents macroeconomic model," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 220-255.
  5. Kauermann Goeran & Krivobokova Tatyana & Semmler Willi, 2011. "Filtering Time Series with Penalized Splines," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-28, March.
  6. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
  7. Blöchl, Andreas, 2014. "Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins," Discussion Papers in Economics 20687, University of Munich, Department of Economics.
  8. Ali M. Mosammam & Jorge Mateu, 2018. "A penalized likelihood method for nonseparable space–time generalized additive models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 333-357, July.
  9. Song, Qiongxia & Yang, Lijian, 2010. "Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2008-2025, October.
  10. Frank van Berkum & Katrien Antonio & Michel Vellekoop, 2021. "Quantifying longevity gaps using micro‐level lifetime data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 548-570, April.
  11. Blöchl, Andreas, 2014. "Trend Estimation with Penalized Splines as Mixed Models for Series with Structural Breaks," Discussion Papers in Economics 18446, University of Munich, Department of Economics.
  12. María Xosé Rodríguez‐Álvarez & María Durbán & Paul H.C. Eilers & Dae‐Jin Lee & Francisco Gonzalez, 2023. "Multidimensional adaptive P‐splines with application to neurons' activity studies," Biometrics, The International Biometric Society, vol. 79(3), pages 1972-1985, September.
  13. Michael Wegener & Göran Kauermann, 2008. "Examining heterogeneity in implied equity risk premium using penalized splines," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 35-56, February.
  14. Sebastian Letmathe, 2022. "Data-driven P-Splines under short-range dependence," Working Papers CIE 152, Paderborn University, CIE Center for International Economics.
  15. Cederbaum, Jona & Scheipl, Fabian & Greven, Sonja, 2018. "Fast symmetric additive covariance smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 25-41.
  16. Cao, Yanrong & Lin, Haiqun & Wu, Tracy Z. & Yu, Yan, 2010. "Penalized spline estimation for functional coefficient regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 891-905, April.
  17. Peter Pütz & Thomas Kneib, 2018. "A penalized spline estimator for fixed effects panel data models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 145-166, April.
  18. Víctor M. Guerrero & Daniela Cortés Toto & Hortensia J. Reyes Cervantes, 2018. "Effect of autocorrelation when estimating the trend of a time series via penalized least squares with controlled smoothness," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 109-130, March.
  19. Dawid, H. & Harting, P. & Neugart, M., 2014. "Economic convergence: Policy implications from a heterogeneous agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 54-80.
  20. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  21. Park, Jun Young & Polzehl, Joerg & Chatterjee, Snigdhansu & Brechmann, André & Fiecas, Mark, 2020. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
  22. Richard Ashley, 2012. "On the Origins of Conditional Heteroscedasticity in Time Series," Korean Economic Review, Korean Economic Association, vol. 28, pages 5-25.
  23. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
  24. Wu, Jingwei & Peng, Hanxiang & Tu, Wanzhu, 2019. "Large-sample estimation and inference in multivariate single-index models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 382-396.
  25. Lauren N. Berry & Nathaniel E. Helwig, 2021. "Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines," Stats, MDPI, vol. 4(3), pages 1-24, September.
  26. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
  27. Israel Martínez‐Hernández & Marc G. Genton, 2021. "Nonparametric trend estimation in functional time series with application to annual mortality rates," Biometrics, The International Biometric Society, vol. 77(3), pages 866-878, September.
  28. Bloechl, Andreas, 2014. "Penalized Splines, Mixed Models and the Wiener-Kolmogorov Filter," Discussion Papers in Economics 21406, University of Munich, Department of Economics.
  29. Fei Qin & Zhangsheng Yu, 2021. "Penalized spline estimation for panel count data model with time-varying coefficients," Computational Statistics, Springer, vol. 36(4), pages 2413-2434, December.
  30. Reiss Philip T. & Huang Lei, 2012. "Smoothness Selection for Penalized Quantile Regression Splines," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-27, May.
  31. Zanin, Luca, 2023. "A flexible estimation of sectoral portfolio exposure to climate transition risks in the European stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
  32. Lee, Wang-Sheng, 2014. "Big and Tall: Is there a Height Premium or Obesity Penalty in the Labor Market?," IZA Discussion Papers 8606, Institute of Labor Economics (IZA).
  33. Göran Kauermann & Timo Teuber & Peter Flaschel, 2012. "Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 409-427, April.
  34. Anusha, "undated". "Evaluating reliability of some symmetric and asymmetric univariate filters," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2015-030, Indira Gandhi Institute of Development Research, Mumbai, India.
  35. Andrada Ivanescu & Ana-Maria Staicu & Fabian Scheipl & Sonja Greven, 2015. "Penalized function-on-function regression," Computational Statistics, Springer, vol. 30(2), pages 539-568, June.
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