Publications of Anton Schick


Research Articles:

2013

[100]
A. Schick and W. Wefelmeyer.
Uniform convergence of convolution estimators for the response density in nonparametric regression.
Bernoulli 19 (2013), 2250--2276.
pdf.     doi.

[99]
O. Y. Savchuk and A. Schick.
Density estimation for power transformations.
Journal of Nonparametric Statistics, 25 (2013), 545--559.

[98]
H. Peng and A. Schick.
An empirical likelihood approach to goodness of fit testing.
Bernoulli 19 (2013), 954-981.
pdf.     doi.

[97]
U. U. Müller, A. Schick and W. Wefelmeyer.
Non-standard behavior of density estimators for functions of independent observations.
Communications in Statistics, Theory and Methods 42 (2013), 2291-2300.
pdf     doi.

[96]
U. U. Müller, A. Schick and W. Wefelmeyer.
Variance bounds for estimators in autoregressive models with constraints.
Statistics, 47 (2013), 477-493.
pdf     doi.

[95]
A. Schick.
Weighted least squares estimation with missing responses: An empirical likelihood approach.
Electronic Journal of Statistics, 7 (2013), 932-945.
doi.

2012

[94]
H. L. Koul, U. U. Müller and A. Schick.
The transfer principle: a tool for complete case analysis.
Annals of Statistics, 60 (2012), 3031-3047.
doi.

[93]
A. Schick and W. Wefelmeyer.
Convergence in weighted $L_1$-norms of convolution estimators for the response density in nonparametric regression.
Journal of the Indian Statistical Association. 50 (2012) 241-261.
pdf.

[92]
A. Schick and W. Wefelmeyer.
On efficient estimation of densities for sums of squared observations.
Statistics & Probability Letters 82 (2012) 1637-1640.
pdf     doi

[91]
U. U. Müller, A. Schick and W. Wefelmeyer.
Estimating the error distribution function in semiparametric additive regression models.
Journal of Statistical Planning and Inference 142 (2012) 552--566.
pdf     doi.

2011

[90]
U. U. Müller, A. Schick and W. Wefelmeyer.
Optimal plug-in estimators for multivariate distributions with conditionally independent components.
Journal of Nonparametric Statistics 23 (2011) 1031-1050.
pdf.     doi

[89]
A. Schick, Y. Wang and W. Wefelmeyer
Tests for normality based on density estimators of convolutions.
Statistics & Probability Letters, 81 (2011), 337-343.
pdf       doi.

[88]
P. E. Greenwood, A. Schick and W. Wefelmeyer.
Estimating the inter-arrival time density in Markov renewal processes under structural assumptions on the transition distribution.
Statistics & Probability Letters, 81 (2011), 277-282.
pdf       doi.

2009

[87]
A. Schick and W. Wefelmeyer.
Non-standard behavior of density estimators for sums of squared observations.
Statistics & Decisions, 27 (2009), 55-73.
Oldenbourg Wissenschaftsverlag, Munich/Germany .
pdf

[86]
H. Peng and A. Schick.
Improving efficient marginal estimators in bivariate models with parametric marginals.
Statistics & Probability Letters, 79 (2009), 2437-2442.
pdf       doi

[85]
A. Schick and W. Wefelmeyer.
Improved density estimators for invertible linear processes.
Communications in Statistics -- Theory and Methods, 38 (2009), 3123-3147.
pdf       doi

[84]
A. Schick and W. Wefelmeyer.
Plug-in estimators for higher-order transition densities in autoregression.
ESAIM: Probability & Statistics, 13 (2009), 135-151.
pdf       doi

[83]
U. U. Müller, A. Schick and W. Wefelmeyer.
Estimating the error distribution function in nonparametric regression with multivariate covariates.
Statistics & Probability Letters, 79 (2009), 957-964.
pdf.       doi

[82]
J. Du and A. Schick.
A covariate-matched estimator of the error variance in nonparametric regression.
Journal of Nonparametric Statistics, 21 (2009), 263-285.
pdf       doi

[81]
U. U. Müller, A. Schick and W. Wefelmeyer.
Estimating the innovation distribution in nonparametric autoregression.
Probability Theory and Related Fields, 144 (2009), 53-77.
pdf       doi

[80]
A. Schick and W. Wefelmeyer.
Convergence rates of density estimators for sums of powers of observations.
Metrika, 69 (2009), 249-264.
pdf       doi

[79]
U. U. Müller, A. Schick and W. Wefelmeyer.
Estimators for alternating nonlinear autoregression.
Journal of Multivariate Analysis, 100 (2009), 266-277.
pdf       doi

2008

[78]
A. Schick and W. Wefelmeyer.
Some developments in semiparametric models.
Journal of Statistical Theory and Practice, 2 (2008), 475-491.
pdf

[77]
A. Schick and W. Wefelmeyer.
Root-n consistency in weighted L1-spaces for density estimators of invertible linear processes.
Statistical Inference for Stochastic Processes, 11 (2008), 281-310.
doi

[76]
U. U. Müller, A. Schick and W. Wefelmeyer.
Estimators For Partially Observed Markov Chains.
September 2006. Revised December 2006.
In: Statistical Models and Methods for Biomedical and Technical Systems (F. Vonta, M. Nikulin, N. Limnios and C. Huber, eds.), (2008), 419-433, Birkhäuser, Boston 2008.
doi

[75]
A. Schick and W. Wefelmeyer.
Convergence rates in weighted L1 spaces of kernel density estimators for linear processes.
ALEA, 4 (2008) 117-129.
pdf

[74]
U. U. Müller, A. Schick and W. Wefelmeyer.
Optimality of estimators for misspecified semi-Markov models.
Stochastics, 80 (2008) 181-196.
pdf

[73]
A. Schick and W. Wefelmeyer.
Prediction in moving average processes.
Journal of Statistical Planning and Inference, 138 (2008) 694-707.
ScienceDirect

2007

[72]
J. Du and A. Schick.
Root-n consistency and functional central limit theorems for estimators of derivatives of convolutions of densities.
International Journal of Statistics and Management Systems, 2 (2007) 67-87.
pdf

[71]
U. U. Müller, A. Schick and W. Wefelmeyer.
Inference for alternating time series.
In: Recent Advances in Stochastic Modeling and Data Analysis (C. H. Skiadas, ed.), 589-596, World Scientific, Singapore 2007.
pdf

[70]
U. U. Müller, A. Schick and W. Wefelmeyer
Estimating the error distribution function in semiparametric regression.
Statistics & Decisions, 25 (2007), 1-18.
Oldenbourg Wissenschaftsverlag, Munich/Germany
pdf

[69]
A. Schick and W. Wefelmeyer.
Uniformly root-n consistent density estimators for weakly dependent invertible linear processes.
Annals of Statistics, 35 (2007), 815-843.
pdf

[68]
A. Schick and W. Wefelmeyer.
Prediction in invertible linear processes.
Statistics & Probability Letters 77 (2007), 1322-1331.
Science Direct

[67]
A. Schick and W. Wefelmeyer.
Root-n consistent density estimators of convolutions in weighted L1-norms.
Journal of Statistical Planning and Inference, 137 (2007) 1765-1774.
ScienceDirect

2006

[66]
U. U. Müller, A. Schick and W. Wefelmeyer
Efficient prediction for linear and nonlinear autoregressive models.
Annals of Statistics, 34 (2006), 2496-2533.
pdf

[65]
A. Schick and W. Wefelmeyer.
Pointwise convergence rates and central limit theorems for kernel density estimators in linear processes.
Statistics & Probability Letters 76 (2006), 1756-1760.
ScienceDirect

[64]
A. Schick and W. Wefelmeyer.
Efficient estimators for time series.
In: Frontiers in Statistics (J. Fan and H. L. Koul, eds.), 45-62, Imperial College Press, London 2006.
pdf

[63]
U.U. Müller, A. Schick and W. Wefelmeyer.
Imputing responses that are not missing.
In Probability, Statistics and Modelling in Public Health (M. Nikulin, D. Commenges and C. Huber, eds.), 350-363. Springer, New York 2006.
pdf

2005

[62]
H. Peng and A. Schick.
Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The least squares approach.
Journal of Multivariate Analysis, 95 (2005), 385-409.

[61]
U.U. Müller, A. Schick and W. Wefelmeyer.
Weighted residual-based density estimators for nonlinear autoregressive models.
Statistica Sinica, 15 (2005), 177-195.
pdf

2004

[60]
H. Peng and A. Schick.
Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The minimum chi-square approach.
Statistics & Decisions, 22 (2004), 301-318.

[59]
A. Schick and W. Wefelmeyer.
Root n consistent density estimators for sums of independent random variables.
Journal of Nonparametric Statistics , 16 (2004), 925-935.

[58]
A. Schick and W. Wefelmeyer.
Functional convergence and optimality of plug-in estimators for stationary densities of moving average processes.
Bernoulli, 10 (2004), 889-917.

[57]
H. Peng and A. Schick. Estimation of linear functionals of bivariate distributions with parametric marginals.
Statistics & Decisions, 22 (2004), 61-77.

[56]
U.U. Müller, A. Schick and W. Wefelmeyer.
Estimating functionals of the error distribution in parametric and nonparametric regression.
Journal of Nonparametric Statistics, 16 (2004), 525-548.

[55]
A. Schick and W. Wefelmeyer.
Estimating invariant laws of linear processes by U-statistics.
Annals of Statistics, 32 (2004), 603-632.
pdf

[54]
A. Schick and W. Wefelmeyer.
Root-n consistent and optimal density estimators for moving average processes.
Scand. J. Statist., 31 (2004), 63-78.

[53]
S. Penev, H. Peng, A. Schick and W. Wefelmeyer.
Efficient estimators for functionals of Markov chains with parametric marginals.
Statistics & Probability Letters, 66 (2004), 335-345.
ScienceDirect

[52]
U.U. Müller, A. Schick and W. Wefelmeyer.
Estimating linear functionals of the error distribution in nonparametric regression.
Journal of Statistical Planning and Inference, 119 (2004), 75-93.
ScienceDirect

2003

[51]
H.L. Koul and A. Schick.
Testing for superiority among two regression curves.
Journal of Statistical Planning and Inference, 117 (2003), 15-33.
ScienceDirect

[50]
A. Schick.
Efficient estimation in a semiparametric heteroscedastic autoregressive model.
In: Crossing Boundaries: Statistical Essays in Honor of Jack Hall. (J. E. Kolassa and D. Oakes, eds).
IMS Lecture Notes-Monograph Series, 43 (2003), 69-86, Institute of Mathematical Statistics, Beachwood, Ohio.

[49]
J. Forrester, W. Hooper, H. Peng and A. Schick.
On the construction of efficient estimators in semiparametric models.
Statistics & Decisions, 21 (2003), 109-138.

[48]
U.U. Müller, A. Schick and W. Wefelmeyer.
Estimating the error variance in nonparametric regression by a covariate-matched U-statistic.
Statistics, 37 (2003), 179-188.

2002

[47]
A. Schick and W. Wefelmeyer.
Efficient estimation in invertible linear processes.
Mathematical Methods of Statistics, 11 (2002), 358-379.

[46]
H. Peng and A. Schick.
On efficient estimation of linear functionals of a bivariate distribution with known marginals.
Statistics and Probability Letters, 59 (2002), 83-91.

[45]
A. Schick and W. Wefelmeyer.
Estimating the innovation distribution in nonlinear autoregressive models.
Annals of the Institute of Statistical Mathematics, 54 (2002), 245-260.

[44]
A. Schick and W. Wefelmeyer.
Estimating joint distributions of Markov chains.
Statistical Inference for Stochastic Processes, 5 (2002), 1-22.

2001

[43]
P.E. Greenwood, A. Schick and W. Wefelmeyer.
Inference for semiparametric models: Some questions and an answer - Comments.
Statistica Sinica, 11 (2001), 892-906.

[42]
U.U. Müller, A. Schick and W. Wefelmeyer.
Plug-in estimators in semiparametric stochastic process models.
Selected Proceedings of the Symposium on Inference in Stochastic Processes (I.V. Basawa, C.C. Heyde and R.L. Taylor, eds).
IMS Lecture Notes-Monograph Series, 37 (2001), 213-234, Institute of Mathematical Statistics, Hayward, California.

[41]
U.U. Müller, A. Schick and W. Wefelmeyer.
Improved estimators for constrained Markov chain models.
Statistics and Probability Letters, 54 (2001), 427-435.

[40]
M. Kessler, A. Schick and W. Wefelmeyer.
The information in the marginal law of a Markov chain.
Bernoulli, 7 (2001), 243-266.

[39]
A. Schick.
Sample splitting with Markov chains.
Bernoulli, 7 (2001), 33-61.

[38]
A. Schick.
On asymptotic differentiability of averages.
Statistics and Probability Letters, 51 (2001), 15-23.

2000

[37]
A. Schick and Q. Yu.
Consistency of the GMLE with mixed case interval-censored data.
Scandinavian Journal of Statistics, 27 (2000), 45-55.

1999

[36]
A. Schick.
Efficient estimation in a semiparametric additive autoregressive model.
Statistical Inference for Stochastic Processes, 2 (1999), 69-98.

[35]
A. Schick and W. Wefelmeyer.
Efficient estimation of invariant distributions of some semiparametric Markov chain models.
Mathematical Methods of Statistics, 8 (1999), 426-440.

[34]
H.L. Koul and A. Schick.
Inference about the ratio of scale parameters in a two-sample setting with current status data.
Statistics and Probability Letters, 45 (1999), 359-369.

[33]
T.C. Lin, M. Pourahmadi and A. Schick.
Regression models with time series errors.
Journal of Time Series Analysis, 20 (1999), 425-433.

[32]
A. Schick.
Efficient estimation in a semiparametric additive regression model with ARMA errors.
In: Asymptotics, Nonparametrics, and Time Series, S. Ghosh ed. (1999), 395-425. Marcel Dekker, New York.

[31]
A. Schick.
Improving weighted least squares estimates in heteroscedastic linear regression when the variance is a function of the mean response.
Journal of Statistical Planning and Inference, 76 (1999), 127-144.

[30]
A. Schick
Efficient estimation of a shift in nonparametric regression.
Statistics & Probability Letters, 41 (1999), 287-301.

1998

[29]
Q. Yu, A. Schick, L. Li and G.Y.C. Wong.
Asymptotic properties of the GMLE in the case 1 interval-censorship model with discrete inspection times.
Canadian Journal of Statistics, 26 (1998), 619-627.

[28]
A. Schick.
An adaptive estimator of the autocorrelation coefficient in regression models with autoregressive errors.
Journal of Time Series Analysis, 15 (1998), 575-589.

[27]
A. Schick.
Estimating a shift in nonparametric regression via U-statistics.
Journal of Statistical Planning and Inference, 67 (1998), 259-271.

[26]
Q. Yu, A. Schick, L. Li and G.Y.C. Wong.
Asymptotic properties of the GMLE with case 2 interval-censored data.
Statistics & Probability Letters, 37 (1998), 223-228.

1997

[25]
H.L. Koul and A. Schick.
Testing for the equality of two nonparametric regression curves.
Journal of Statistical Planning and Inference, 65 (1997), 293-314.

[24]
H.L. Koul and A. Schick.
Efficient estimation in nonlinear autoregressive time series models.
Bernoulli, 3 (1997), 247-277.

[23]
A. Schick.
On U-statistics with random kernels.
Statistics & Probability Letters, 34 (1997), 275-284.

[22]
A. Schick.
Efficient estimates in linear and nonlinear regression with heteroscedastic errors.
Journal of Statistical Planning and Inference, 58 (1997), 371-387.

1996

[21]
A. Schick.
Root-n consistent estimation in a random coefficient autoregressive model.
The Australian Journal of Statistics, 38 (1996), 155-160.

[20]
H.L. Koul and A. Schick.
Adaptive estimation in a random coefficient autoregressive model.
Annals of Statistics, 24 (1996), 1025-1052.

[19]
A. Schick.
Root-n consistent and efficient estimation in semiparametric additive regression models.
Statistics and Probability Letters, 30 (1996), 45-51.

[18]
A. Schick.
Efficient estimation in a semiparametric additive regression model with autoregressive errors.
Stochastic Processes and their Applications, 61 (1996), 339-361.

[17]
A. Schick.
Root-n consistent estimation in partly linear regression models.
Statistics and Probability Letters, 28 (1996), 353-358.

[16]
S. Choi, W.J. Hall and A. Schick.
Asymptotically uniformly most powerful tests in parametric and semiparametric models.
Annals of Statistics, 24 (1996), 841-861.

[15]
A. Schick.
Weighted least squares estimates in partly linear regression models.
Statistics and Probability Letters, 27 (1996), 281-287.

1994

[14]
A. Schick.
Estimation of the autocorrelation coefficient in the presence of a regression trend.
Statistics and Probability Letters, 21 (1994), 371-380.

[13]
A. Schick.
Efficient estimation in regression models with unknown scale functions.
Mathematical Methods of Statistics, 3 (1994), 171-212.

1993

[12]
A. Schick.
On efficient estimation in regression models.
Annals of Statistics, 21 (1993), 1486-1521. Correction and Addendum 23 (1995),1862-1863.

1992

[11]
R.A. Johnson, C.H. Morrell and A. Schick.
Two sample nonparametric estimation and confidence intervals under truncation.
Biometrics, 48 (1992), 1043-56.

1991

[10]
K.G. Mehrotra, A. Schick and P. Jackson.
On choosing an optimally trimmed mean.
Communications in Statistics - Simulation and Computation, 20 (1991), 73-80.

1990

[09]
A. Schick and V. Susarla.
Inference with paired data and partial control.
Communications in Statistics - Theory and Methods, 19 (1990), 3901-3913.

[08]
A. Schick and V. Susarla.
An infinite dimensional convolution theorem with applications to random censoring and missing data models.
Journal of Statistical Planning and Inference, 24 (1990), 13-23.

[07]
K.G. Mehrotra, A. Schick and V. Susarla.
Estimation in two sample type II censoring models.
Statistics and Probability Letters, 8 (1990), 13-22.

1988

[06]
A. Schick, V. Susarla and H.L. Koul.
Efficient estimation of functionals with censored data.
Statistics and Decisions , 6 (1988), 349-360.

[05]
A. Schick.
On estimation in LAMN families when there are nuisance parameters present.
Sankhya, 50 (1988), Series A, 249-268.

[04]
A. Schick and V. Susarla.
Efficient estimation in some missing data problems.
Journal of Statistical Planning and Inference, 19 (1988), 217-228.

1987

[03]
A. Schick and V. Susarla.
A k-sample problem with censored data.
In Mathematical Statistics and Probability Theory, Volume B, Statistical Inference and Methods. P. Bauer, F. Konecny and W. Wertz, eds., (1987) 215-230. Reidel, Dordrecht.

[02]
A. Schick.
A note on the construction of asymptotically linear estimators.
Journal of Statistical Planning and Inference, 16 (1987), 89-105. Correction (1989), 22, 269-270.

1986

[01]
A. Schick.
On asymptotically efficient estimation in semiparametric models.
Annals of Statistics, 14 (1986), 1139-1151.


Book Reviews:

A. Schick.
A Review of ``Efficient and Adaptive Estimation for Semiparametric Models''.
Journal of the American Statistical Association, 89 (1994), 1565-1566.


Homepage of Anton Schick
Preprints of Anton Schick
Last updated December 31, 2010