Xavier de Luna

Professor of Statistics at Umeå School of Business, Economics and Statistics - Umeå University, Sweden

Email: xavier.deluna@umu.se



Page index: research | teaching | students | working papers | publications | brief CV



Research interests and projects:

Theory and method: Causal inference in observational studies; Model selection and validation; Incomplete data; Life course studies.

Fields of application: Register based research in the social and medical sciences; Evaluation studies; Life course studies of health and welfare; brain imaging.

Current projects:

Editorial boards:

Teaching:

  • Master course: Causal inference in register studies

PhD students:

  • Niloofar Moosavi
  • Tetiana Gorbach
  • New PhD students positions are available!

Completed:

  • Philip Fowler (PhD, 2017: Methods for improving covariate balance in observational studies)
  • Minna Genbäck (PhD, 2016: Uncertainty intervals and sensitivity analysis for missing data)
  • Maria Josefsson (PhD, 2013: Attrition in studies of cognitive ageing)
  • Mathias Lundin (PhD, 2011: Sensistivity analysis of untestable assumptions in causal inference)
  • Jenny Häggström (PhD, 2011: Selection of smoothing parameters with application in causal inference)
  • Suad Elezovic (PhD, 2009: Modeling financial volatility: A functional approach with applications to Swedish limit order book data)
  • Ingeborg Waernbaum (PhD, 2008: Covariate selection and propensity score specification in causal inference )

Working papers:

Selected publications:

Method

  • Josefsson, M., de Luna, X., Daniels, M.J., and Nyberg, L. (2016) Causal inference with longitudinal outcomes and non-ignorable dropout: Estimating the effect of living alone on cognitive decline. Journal of the Royal Statistical Society: Series C, 65(1): 131-144. DOI: 10.1111/rssc.12110
  • Häggström, J., Persson, E., Waernbaum, I., & de Luna, X. (2015). CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects. Journal of Statistical Software, 68(1), 1 - 20. doi:http://dx.doi.org/10.18637/jss.v068.i01
  • Genbäck, M., Stanghellini, E. and de Luna, X. (2015) Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome. Statistical Papers, 56, 3, 829-847 DOI: 10.1007/s00362-014-0610-x
  • de Luna, X. and Johansson, P. (2014) Testing the unconfoundedness assumption using an instrumental assumption. Journal of Causal Inference. DOI: 10.1515/jci-2013-0011
  • Häggström, J. and de Luna, X. (2014) Targeted Smoothing Parameter Selection for Estimating Average Causal Effects. Computational Statistics. DOI: 10.1007/s00180-014-0515-0
    Preprint. arXiv:1306.4509
  • de Luna, X. and Lundin, M. (2014) Sensitivity analysis of the unconfoundedess assumption with an application to an evaluation of college choice effects on earnings. J of Applied Statistics. DOI: 10.1080/02664763.2014.890178
  • de Luna, X. and Johansson, P. (2010), Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences, Journal of Statistical Planning and Inference 140, 2122-2137. Paper and Erratum.
  • de Luna, X. and Häggström, J. (2010), Estimating Prediction Error: Cross-Validation vs. Accumulated Prediction Error, Communications in statistics. Simulation and computation 39, 880-898.

  • de Luna, X., Johansson, P., and Sjöstedt-de Luna, S. (2010) Bootstrap inference for K-nearest neighbour matching estimators. IZA Discussion Papers 5361, Institute for the Study of Labor, Bonn. Download pdf.
  • de Luna, X. and Johansson, P. (2008) Graphical diagnostics of endogeneity, In Advances in Econometrics, Vol. 21: Modeling and Evaluating Treatment Effects in Econometrics, Millimet, D.L, Smith, J.A. and Vytlacil E. (Eds), pp.147-166.

  • de Luna, X and Johansson, P. (2006), Exogeneity in structural equation models, Journal of Econometrics, 132, 527-543.

  • Cantoni, E. and de Luna, X. (2006), Non-parametric adjustment for covariates when estimating a treatment effect, Journal of Nonparametric Statistics, 18, 227-244.

  • de Luna, X. and Genton, M.G. (2005), Predictive spatio-temporal models for spatially sparse environmental data, Statistica Sinica, 15, 547-568.
  • de Luna, X. and Genton, M.G. (2004), Spatio-temporal autoregressive models for US unemployment rate, in Advances in Econometrics: Spatial and Spatiotemporal Econometrics, J. P. Lesage, R. K. Pace (eds), Elsevier, Vol. 18, 283-298.

  • de Luna, X. and Skouras, K. (2003), Choosing a Model Selection Strategy. Scandinavian Journal of Statistics, 30, 113-128.

  • Bask, M. and de Luna, X. (2002), Characterizing the degree of stability of non-linear dynamic models, Studies in Nonlinear Dynamics and Econometrics, Vol. 6: No. 1, Article 3. Download www.bepress.com/snde/vol6/iss1/art3/.

  • de Luna, X. and Genton, M.G. (2002), Simulation-based Inference for Simultaneous Processes on Regular Lattices, Statistics and Computing, 12, 125-134.

  • de Luna, X. (2001), Guaranteed-content Prediction Intervals for Non-linear Autoregressions, Journal of Forecasting, 20, 265-272.

  • de Luna, X. and Genton, M.G. (2001), Robust Simulation-Based Estimation of ARMA Models. Journal of Computational and Graphical Statistics, 10, 370-387.
  • de Luna, X. (2000) Prediction Intervals Based on Autoregression Forecasts, Journal of the Royal Statistical Society, Series D, 49, 87-93.

  • Genton, M.G. and de Luna, X. (2000), Robust Simulation-based Estimation, Statistics and Probability Letters, 48, 253-259.

  • Brännäs, K. and de Luna, X. (1998), Generalized Method of Moment and Indirect Estimation of the ARasMA Model, Computational Statistics, 13, 485-494.

  • de Luna, X. (1998), An Improvement of Akaike's FPE Criterion to Reduce its Variability, Journal of Time Series Analysis, 19, 457-472.

  • de Luna, X. (1998), Projected Polynomial Autoregression for Prediction of Stationary Time Series, Journal of Applied Statistics, 25, 763-776.

Applications

  • Chaparro, P.M. et al. (2017) Childhood family structure and women's adult overweight risk: A longitudinal study, Scandinavian J of Epidemiology, 10.1177/1403494817705997.
  • Svensson, I., Lundholm, E., de Luna, X., Malmberg, G. (2015) Family Life Course and the Timing of Women's Retirement: a Sequence Analysis Approach. Population, Space and Place. PDF. DOI: 10.1002/psp.1950
  • Chaparro, P.M., Ivarsson, A., Koupil, I., Nilsson, K., Häggström, J., de Luna, X., Lindgren, U. (2015) Regional inequalities in overweight and obesity among first-time pregnant women in Sweden, 1992–2010. Scandinavian Journal of Public Health, 43(5): 534-539.
  • Stenberg, A., de Luna, X., Westerlund, O. (2014) Does formal education for older workers increase earnings? Evidence based on rich data and long-term follow up. Labour 28(2), 163-189. DOI: 10.1111/labr.12030
     
  • Pudas, S., Persson J., Josefsson, M., de Luna X., Nilsson L.-G., Nyberg, L. (2013) Brain characteristics of individuals resisting age-related cognitive decline over two decades. J of Neuroscience, 33(20): 8668-8677.
  • Josefsson, M., de Luna, X., Pudas, S., Nilsson, L.G., Nyberg, L. (2012) Genetic and lifestyle predictors of 15-year longitudinal change in episodic memory. J of American Geriatrics Society 60(12), 2308–2312.
  • Stenberg, A., de Luna, X., and Westerlund, O. (2012) Can Adult Upper Secondary Education Delay Retirement from the Labour Market? Journal of Population Economics 25(2), 2012, 677-696. (WP version: IFAU 2010:2).
  • de Luna, X., Forslund, A., and Liljeberg, L. (2008) Effekter av yrkesinriktad arbetsmarknadsutbildning f?r deltagare under perioden 2002-04, IFAU Working paper 2008:1, Institute for Labour Market Policy Evaluation (IFAU). Download pdf.
  • Daunfeldt, S.-O. and de Luna, X. (2008), Central Bank Independence and Price Stability: Evidence from 23 OECD-countries, Oxford Economic Papers 60, 410-422.
  • Bask, M. and de Luna, X. (2005), EMU and the Stability and Volatility of Foreign Exchange: Some Empirical Evidence, Chaos, Solitons & Fractals, 25, 737-750.
  • Daunfeldt, S.-O. and de Luna, X. (2001),The Efficacy and Cost of Regime Shifts in Inflation Policies: Evidence from New Zealand and Sweden, Applied Economics, 33, 217-224.

Brief CV

Currently:
Professor at the Department of Statistics, Umeå University.
Chair of the steering group of the Umeå SIMSAM Lab and of Stat4Reg Lab.
Member of the scientific advisory board of Statistics Sweden.
Associate Editor for scientific journals: Biometrics and Observational Studies

2014: Guest researcher at Research Center of Statistics, University of Geneva.
2007- : Professor of Statistics, Umeå University, Sweden.
2008-2015 : Affiliated researcher at the Institute of Labour Market Policy Evaluation (IFAU), Uppsala.
2009-2011: Head of the Department of Statistics, Umeå University, Sweden.
2007-08 : Research Fellow at the Institute of Labour Market Policy Evaluation (IFAU), Uppsala.
2006: Guest researcher at Center for Statistics and the Social Sciences, University of Washington, Seattle.
2002-06 : Associate Professor at the Dept. of Statistics, Umeå University, Sweden.
2001-2002 : Assistant Professor at the Dept. of Statistics, Umeå University, Sweden.
2000-2002 : Statistical consultant at the Ume? School of Business, Umeå University, Sweden.
1999-2001 : Research Fellow at the Dept. of Economics, Umeå University, Sweden. Research funded by the Wikström's Foundation (1999-2000), and the Wallander and Hedelius' Foundation (2000-2001).
1997-1999: Lecturer in Statistics, Dept. of Statistical Science, University College London, UK.
1996-1997: Post-doctoral research fellow at the Dept. of Economics, Umeå University, Sweden. Funded by the Swiss National Science Foundation.
1996: Docteur ès sciences (PhD in Statistics), Swiss Federal Institute of Technology, Lausanne, Switzerland.


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