Research interests
My research is currently supported by NSF grant DMS-0907014.
[Most articles are
listed in MathSciNet]
[xx] U.U. Müller, A. Schick and W. Wefelmeyer.
Variance bounds for estimators in autoregressive models with
constraints. To appear in: Statistics.
Available online:
doi:10.1080/02331888.2011.616932 (27 September 2011).
[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] 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.
[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.
[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.
[25] U.U. Müller (2009).
Estimating linear functionals in nonlinear regression with responses
missing at random.
Ann. Statist.,
37, 2245-2277.
[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.
[23] U.U. Müller, A. Schick and W. Wefelmeyer
(2009).
Estimating the innovation distribution in nonparametric autoregression.
Probab. Theory Related Fields, 144, 53-77.
[22] U.U. Müller, A. Schick and W. Wefelmeyer
(2009).
Estimators for alternating nonlinear autoregression.
J. Multivariate Anal., 100, 266-277.
[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. [20] U.U. Müller, A. Schick and W. Wefelmeyer
(2008).
Optimality of estimators for misspecified semi-Markov models.
Stochastics, 80, 2, 181-196.
[17] U.U. Müller, A. Schick and W. Wefelmeyer (2006).
Efficient prediction for linear and nonlinear autoregressive models. Ann. Statist., 34, 5,
2496-2533.
[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. [15] U.U. Müller, A. Schick and W. Wefelmeyer
(2005). Weighted residual-based density estimators for nonlinear
autoregressive models.
Statist. Sinica, 15,
177-195. [14] P.E. Greenwood, U.U. Müller and L.M. Ward
(2004). Soft threshold stochastic resonance. Phys.
Rev. E, 70, 051110. [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. [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. [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. [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. [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. [8] U.U. Müller and G. Osius (2003). Asymptotic
normality of goodness-of-fit statistics for sparse Poisson data. Statistics,
37, 2, 119-143. [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. [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. [5] U.U. Müller and W. Wefelmeyer (2002).
Estimators for models with constraints involving unknown parameters. Math. Meth.
Stat., 11, 2, 221-235. [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. [3] U.U. Müller, A. Schick and W. Wefelmeyer
(2001). Improved estimators for constrained Markov chain models.
Statist. Probab. Lett., 54, 4, 427-435. [2] U.U. Müller (2000). Nonparametric regression
for threshold data.
Can.
J. Stat., 28, 2, 301-310.
[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. Submitted papers
H.L. Koul, U.U. Müller and A. Schick.
Complete case analysis revisited.
U.U. Müller, A. Schick and W. Wefelmeyer.
Efficient estimators for alternating quasi-likelihood models.
U.U. Müller and I. Van Keilegom.
Efficient parameter estimation in regression with missing responses.
U.U. Müller, A. Schick and W. Wefelmeyer.
Non-Standard Behavior of Density Estimators for Functions of Independent
Observations.
J. Wei, R.J. Carroll, U.U. Müller, I. Van Keilegom, N. Chatterjee.
Robust Estimation for Homoscedastic Regression in the Secondary Analysis
of Case-Control Data.
Theses
U. Müller (1997). Asymptotic Normality of
Goodness-of-Fit Statistics for Sparse Poisson and Case-Control Data.
Ph.D. thesis, Universität Bremen. U. Müller (1993). Experimente zum Auffinden relevanter
Einflussfaktoren. Diploma thesis, Freie Universität Berlin, 74 pp.
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. U.U. Müller, A. Schick and W. Wefelmeyer (2004).
Estimating the error distribution function in nonparametric regression.
Technical report. 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. U. Müller and G. Osius (1998). Asymptotic normality of
goodness-of-fit statistics for sparse Poisson data. Universität
Bremen, Mathematik-Arbeitspapiere A, 51,
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[19] U.U. Müller, A. Schick and W. Wefelmeyer
(2007).
Estimating the error distribution function in semiparametric
regression.
Statist. Decisions,
25, 1,
1-18.
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[18] U.U. Müller (2007). Weighted
least squares estimators in possibly misspecified nonlinear regression.
Metrika, 66, 1, 39-59.
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(Sept 2011)
29 pp. (plus tables)
(Mar 2011)
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©Uschi
Mueller-Harknett, last update: February 2012