Ursula U. Müller

Ursula U. Müller - Research

Research interests

regression and stochastic process models

nonparametric and semiparametric inference

asymptotic efficiency

missing data

Published and accepted papers (refereed)

[Most articles are listed in MathSciNet]

[xx] G. Dai, U.U. Müller and R.J. Carroll (2024+). Penalized regression with multiple loss functions and variable selection by voting. To appear in: Statist. Sinica.
41 pp. doi

[50] U.U. Müller, A. Schick and W. Wefelmeyer (2024). Estimation for Markov chains with periodically missing observations. J. Time Ser. Anal., 45, 1006-1019.
14 pp. .pdf doi

[49] G. Dai, U.U. Müller and R.J. Carroll (2023). Data integration in high dimension with multiple quantiles. Statist. Sinica, 33, 169-192.
24 pp. doi

[48] G. Dai and U.U. Müller (2019). Efficient estimators for expectations in nonlinear parametric regression models with responses missing at random. Electron. J. Stat., 13, 3985-4014.
30 pp. .pdf doi

[47] U.U. Müller and I. Van Keilegom (2019). Goodness-of-fit tests for the cure rate in a mixture cure model. Biometrika, 106, 211-227.
17 pp. .pdf supplement (7 pp.) doi

[46] U.U. Müller, H. Peng and A. Schick (2019). Inference about the slope in linear regression: an empirical likelihood approach. Ann. Inst. Statist. Math., 71, 181-211.
29 pp. .pdf doi

[45] J. Chown and U.U. Müller (2018). Detecting heteroskedasticity in nonparametric regression using weighted empirical processes. J. R. Stat. Soc. Ser. B., 80, 951-974.
article (25 pp.) doi supplement (3 pp.) corrigendum (2 pp.) doi

[44] U.U. Müller and A. Schick (2018). Efficiency for heteroscedastic regression with responses missing at random. J. Statist. Plann. Inference, 196, 132-143.
18 pp. .pdf doi

[43] H.L. Koul, U.U. Müller and A. Schick (2017). Estimating the error distribution in a single-index model. From Statistics to Mathematical Finance, Festschrift in Honour of Winfried Stute (D. Ferger, W. González Manteiga, T. Schmidt, J.-L. Wang, eds.), Springer, 209-234.
21 pp. .pdf

[42] U.U. Müller and A. Schick (2017). Efficiency transfer for regression models with responses missing at random. Bernoulli, 23, 2693-2719.
27 pp. .pdf doi

[41] U.U. Müller, A. Schick and W. Wefelmeyer (2016). Density estimators for the convolution of discrete and continuous random variables. Ann. I.S.U.P., 60, 55-65.
11 pp. .pdf

[40] U.U. Müller, A. Schick and W. Wefelmeyer (2015). Estimators in step regression models. Statist. Probab. Lett., 100, 124-129.
10 pp. .pdf doi

[39] U.U. Müller and W. Wefelmeyer (2014). Estimating a density under pointwise constraints on the derivatives. Math. Meth. Statist., 23, 201-209.
11 pp. .pdf doi

[38] U.U. Müller, A. Schick and W. Wefelmeyer (2014). Testing for additivity in partially linear regression with possibly missing responses. J. Multivariate Anal., 128, 51-61.
18 pp. .pdf doi

[37] U.U. Müller and I. Van Keilegom (2014). Efficient quantile regression with auxiliary information. Contemporary Developments in Statistical Theory, Springer Proceedings in Mathematics & Statistics, 68, 365-374.
10 pp. .pdf doi

[36] U.U. Müller, A. Schick and W. Wefelmeyer (2014). Efficient estimators for alternating quasi-likelihood models. J. Indian Statist. Assoc., 52, 1-17.
17 pp. .pdf

[35] J. Chown and U.U. Müller (2013). Efficiently estimating the error distribution in nonparametric regression with responses missing at random. J. Nonparametr. Statist., 25, 3, 665-677.
15 pp. .pdf doi

[34] U.U. Müller, A. Schick and W. Wefelmeyer (2013). Non-standard behavior of density estimators for functions of independent observations. In: Stochastic Modeling Techniques and Data Analysis International Conference, Comm. Statist. Theory Methods, 42, 2291-2300.
10 pp. .pdf doi

[33] U.U. Müller, A. Schick and W. Wefelmeyer (2013). Variance bounds for estimators in autoregressive models with constraints. Statistics, 47, 3, 477-493.
21 pp. .pdf doi

[32] J. Wei, R.J. Carroll, U.U. Müller, I. Van Keilegom and N. Chatterjee (2013). Robust Estimation for Homoscedastic Regression in the Secondary Analysis of Case-Control Data. J. R. Stat. Soc. Ser. B, 75, 185-206.
22 pp. .pdf doi

[31] H.L. Koul, U.U. Müller and A. Schick (2012). The transfer principle: a tool for complete case analysis. Ann. Statist., 40, 3031-3049.
19 pp. .pdf doi

[30] U.U. Müller and I. Van Keilegom (2012). Efficient parameter estimation in regression with missing responses. Electron. J. Stat., 6, 1200-1219.
20 pp. .pdf doi

[29] U.U. Müller (2012). Estimating the density of a possibly missing response variable in nonlinear regression. J. Statist. Plann. Inference, 142, 1198-1214.
28 pp. .pdf doi

[28] U.U. Müller, A. Schick and W. Wefelmeyer (2012). Estimating the error distribution function in semiparametric additive regression models. J. Statist. Plann. Inference, 142, 552-566.
22 pp. .pdf doi

[27] U.U. Müller, A. Schick and W. Wefelmeyer (2011). Optimal plug-in estimators for multivariate distributions with conditionally independent components. J. Nonparametr. Statist., 23, 1031-1050.
23 pp. .pdf doi

[26] U.U. Müller and W. Wefelmeyer (2010). Estimation in nonparametric regression with nonregular errors. In: Recent Advances in Statistical Inference. In Honor of Professor Masafumi Akahira (M. Aoshima, ed.), Comm. Statist. Theory Methods, 39, 1619-1629.
13 pp. .pdf doi

[25] U.U. Müller (2009). Estimating linear functionals in nonlinear regression with responses missing at random. Ann. Statist., 37, 2245-2277.
33 pp. .pdf doi

[24] U.U. Müller, A. Schick and W. Wefelmeyer (2009). Estimating the error distribution function in nonparametric regression with multivariate covariates. Statist. Probab. Lett., 79, 957-964.
11 pp. .pdf doi

[23] U.U. Müller, A. Schick and W. Wefelmeyer (2009). Estimating the innovation distribution in nonparametric autoregression. Probab. Theory Related Fields, 144, 53-77.
25 pp. .pdf doi

[22] U.U. Müller, A. Schick and W. Wefelmeyer (2009). Estimators for alternating nonlinear autoregression. J. Multivariate Anal., 100, 266-277.
19 pp. .pdf doi

[21] U.U. Müller, A. Schick and W. Wefelmeyer (2008). Estimators for partially observed Markov chains. Statistical Models and Methods for Biomedical and Technical Systems (F. Vonta, M. Nikulin, N. Limnios and C. Huber, eds.), Birkhäuser, Boston, 423-438.
15 pp. .pdf doi

[20] U.U. Müller, A. Schick and W. Wefelmeyer (2008). Optimality of estimators for misspecified semi-Markov models. Stochastics, 80, 2, 181-196.
13 pp. .pdf doi

[19] U.U. Müller, A. Schick and W. Wefelmeyer (2007). Estimating the error distribution function in semiparametric regression. Statist. Decisions, 25, 1, 1-18.
21 pp. .pdf doi

[18] U.U. Müller (2007). Weighted least squares estimators in possibly misspecified nonlinear regression. Metrika, 66, 39-59.
22 pp. .pdf doi

[17] U.U. Müller, A. Schick and W. Wefelmeyer (2006). Efficient prediction for linear and nonlinear autoregressive models. Ann. Statist., 34, 5, 2496-2533.
38 pp. .pdf doi

[16] U.U. Müller, A. Schick and W. Wefelmeyer (2006). Imputing responses that are not missing. Probability, Statistics and Modelling in Public Health, Symposium in Honor of Marvin Zelen (M. Nikulin, D. Commenges and C. Huber, eds.), 350-363, Springer.
14 pp. .pdf link

[15] U.U. Müller, A. Schick and W. Wefelmeyer (2005). Weighted residual-based density estimators for nonlinear autoregressive models. Statist. Sinica, 15, 177-195.
20 pp. .pdf link

[14] P.E. Greenwood, U.U. Müller and L.M. Ward (2004). Soft threshold stochastic resonance. Phys. Rev. E., 70, 051110.
20 pp. .pdf doi

[13] P.E. Greenwood, U.U. Müller and W. Wefelmeyer (2004). An introduction to efficient estimation for semiparametric time series. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life (M. S. Nikulin, N. Balakrishnan, M. Mesbah and N. Limnios, eds.), 253-272, Statistics for Industry and Technology, Birkhäuser, Basel.
16 pp. .pdf

[12] U.U. Müller, A. Schick and W. Wefelmeyer (2004). Estimating functionals of the error distribution in parametric and nonparametric regression. J. Nonparametr. Stat., 16, 525-548.
25 pp. .pdf doi

[11] P.E. Greenwood, U.U. Müller and W. Wefelmeyer (2004). Efficient estimation for semiparametric semi-Markov processes. In: Semi-Markov Processes and Their Applications (N. Limnios, ed.), Comm. Statist. Theory Meth., 33, 419-435.
17 pp. .pdf doi

[10] U.U. Müller, A. Schick and W. Wefelmeyer (2004). Estimating linear functionals of the error distribution in nonparametric regression. J. Statist. Plann. Inference, 119, 75-93.
20 pp. .pdf doi

[9] U.U. Müller, A. Schick and W. Wefelmeyer (2003). Estimating the error variance in nonparametric regression by a covariate-matched U-statistic. Statistics, 37, 3, 179-188.
11 pp. .pdf doi

[8] U.U. Müller and G. Osius (2003). Asymptotic normality of goodness-of-fit statistics for sparse Poisson data. Statistics, 37, 2, 119-143.
27 pp. .pdf doi

[7] P.E. Greenwood, U.U. Müller, L.M. Ward and W. Wefelmeyer (2003). Statistical analysis of stochastic resonance in a thresholded detector. Austrian J. Stat., 32, 1 & 2, 49 - 70.
22 pp. .pdf

[6] U.U. Müller and W. Wefelmeyer (2002). Autoregression, estimating functions, and optimality criteria. In: Advances in Statistics, Combinatorics and Related Areas (C. Gulati, Y.-X. Lin, J. Rayner and S. Mishra, eds.), 180-195, World Scientific Publishing, Singapore.
16 pp. .pdf

[5] U.U. Müller and W. Wefelmeyer (2002). Estimators for models with constraints involving unknown parameters. Math. Meth. Stat., 11, 2, 221-235.
17 pp. .pdf

[4] U.U. Müller, A. Schick and W. Wefelmeyer (2001). Plug-in estimators in semiparametric stochastic process models. In: Selected Proceedings of the Symposium on Inference for Stochastic Processes (I. V. Basawa, C. C. Heyde and R. L. Taylor, eds.), 213-234, IMS Lecture Notes-Monograph Series, 37, Institute of Mathematical Statistics, Beachwood, Ohio.
22 pp. .pdf

[3] U.U. Müller, A. Schick and W. Wefelmeyer (2001). Improved estimators for constrained Markov chain models. Statist. Probab. Lett., 54, 4, 427-435.
9 pp. .pdf doi

[2] U.U. Müller (2000). Nonparametric regression for threshold data. Can. J. Stat., 28, 2, 301-310.
12 pp. .pdf doi

[1] U.U. Müller and L.M. Ward (2000). Stochastic resonance in a statistical model of a time-integrating detector. Phys. Rev. E, 61, 4, 4286-4294.
9 pp. .pdf doi

Theses

U.U. Müller (2005). Optimal estimation in regression and autoregression models. Habilitationsschrift (cumulative), Universität Bremen.

U. Müller (1997). Asymptotic Normality of Goodness-of-Fit Statistics for Sparse Poisson and Case-Control Data. Doctoral thesis, Universität Bremen.
150 pp. .pdf.

U. Müller (1993). Experimente zum Auffinden relevanter Einflussfaktoren. Diploma thesis, Freie Universität Berlin.

Other papers and reports (not refereed)

U.U. Müller, A. Schick and W. Wefelmeyer (2007). Inference for alternating time series. In: Recent Advances in Stochastic Modeling and Data Analysis (C.H. Skiadas, ed.), 589-596, World Scientific, Singapore.
8 pp. .pdf

U.U. Müller, A. Schick and W. Wefelmeyer (2004). Estimating the error distribution function in nonparametric regression. Technical report. Available in arXiv:1810.01645.
20 pp. .pdf

U.U. Müller (2000). Goodness-of-fit statistics for large numbers of cells. In: Second International Conference on Mathematical Models in Reliability, Bordeaux, Universite Victor Sengalen, Bordeaux, France, July 4-7, 2000. Abstracts Book, Vol. 2, pp. 792-795.

U.U. Müller (1999). Nonparametric regression for threshold data. Universität Bremen, Mathematik-Arbeitspapiere A, 52.
18 pp. .pdf

U. Müller and G. Osius (1998). Asymptotic normality of goodness-of-fit statistics for sparse Poisson data. Universität Bremen, Mathematik-Arbeitspapiere A, 51.
15 pp.