[100]
A. Schick and W. Wefelmeyer.
Uniform convergence of convolution estimators for the response density in nonparametric regression.
Bernoulli 19 (2013), 2250--2276.
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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.
Empirical likelihood approach to goodness of fit testing.
Bernoulli 19 (2013), 954-981.
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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.
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doi.
[95]
A. Schick.
Weighted least squares estimation with missing responses:
An empirical likelihood approach.
Electronic Journal of Statistics, 7 (2013), 932-945.
doi.
[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.
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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.
[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.
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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.
[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
[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.
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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
[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
[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.
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[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.
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[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
[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.
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[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
[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.
[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.
[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.
[37]
A. Schick and Q. Yu.
Consistency of the GMLE with mixed case interval-censored data.
Scandinavian Journal of Statistics, 27 (2000), 45-55.
[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.
[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.
[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.
[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.
[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.
[12]
A. Schick.
On efficient estimation in regression models.
Annals of Statistics, 21 (1993), 1486-1521.
Correction and Addendum 23 (1995),1862-1863.
[11]
R.A. Johnson, C.H. Morrell and A. Schick.
Two sample nonparametric estimation and confidence intervals under truncation.
Biometrics, 48 (1992), 1043-56.
[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.
[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.
[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.
[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.
[01]
A. Schick.
On asymptotically efficient estimation in semiparametric models.
Annals of Statistics, 14 (1986), 1139-1151.
A. Schick.
A Review of ``Efficient and Adaptive Estimation for Semiparametric Models''.
Journal of the American Statistical Association, 89 (1994), 1565-1566.