Abstract
Estimating the centre of a symmetric distribution is one of the basic and important problems in statistics. Given a random sample from the symmetric distribution, natural estimators of the centre are the sample mean and sample median. However, these two estimators are either not robust or inefficient. Other estimators, such as Hodges-Lehmann estimator (Hodges and Lehmann, Ann Math Stat 34:598–611, 1963), the location M-estimator (Huber, Ann Math Stat 35:73–101, 1964) and Bondell (Commun Stat Theory Methods 37:318–327, 2008)’s estimator, were proposed to achieve high robustness and efficiency. In this paper, we propose an estimator by maximizing a smoothed likelihood. Simulation studies show that the proposed estimator has much smaller mean square errors than the existing methods under uniform distribution, t-distribution with one degree of freedom, and mixtures of normal distributions on the mean parameter, and is comparable to the existing methods under other symmetric distributions. A real example is used to illustrate the proposed method. The R code for implementing the proposed method is also provided.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bondell HD (2008) On robust and efficient estimation of the center of symmetry. Commun Stat Theory Methods 37:318–327
Eggermont PPB, LaRiccia VN (1995) Maximum smoothed likelihood density estimation for inverse problems. Ann Stat 23:199–220
Eggermont PPB, Lariccia VN (2000) Maximum likelihood estimation of smooth monotone and unimodal densities. Ann Stat 28:922–947
Eggermont PPB, Lariccia VN (2001) Maximum penalized likelihood estimation: volume I: density estimation. Springer, New York
Hodges JL, Lehmann EL (1963) Estimates of location based on rank tests. Ann Math Stat 34:598–611
Huber PJ (1964) Robust estimation of a location parameter. Ann Math Stat 35:73–101
Levine M, Hunter D, Chauveau D (2011) Maximum smoothed likelihood for multivariate mixtures. Biometrika 98:403–416
Naylor JC, Smith AFM (1983) A contamination model in clinical chemistry: an illustration of a method for the efficient computation of posterior distributions. J R Stat Soc Ser D 32:82–87
Niu X, Li P, Zhang P (2016) Testing homogeneity in a scale mixture of normal distributions. Stat Pap 57:499–516
Owen AB (2001) Empirical likelihood. Chapman and Hall/CRC, New York
R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Wand MP, Jones MC (1995) Kernel smoothing. Chapman and Hall, London
Yu T, Li P, Qin J (2014) Maximum smoothed likelihood component density estimation in mixture models with known mixing proportions. arXiv preprint, arXiv:1407.3152
Acknowledgements
Dr. Li’s work is partially supported by the Natural Sciences and Engineering Research Council of Canada grant No RGPIN-2015-06592.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: R code for calculating \(\hat{\mu }_{Smo}\)
Appendix: R code for calculating \(\hat{\mu }_{Smo}\)
library("KernSmooth")
library("ICSNP")
norm.kern=function(x,data,h)
{
out=mean( dnorm( (x-data)/h ) )/h
out
}
dkern=function(x,data)
{
h=dpik(data,kernel="normal")
out=lapply(x,norm.kern,data=data,h=h)
as.numeric(out)
}
dfint=function(x,data,mu)
{
p1=dkern(x,data)
p2=log(0.5*dkern(x,data)+0.5*dkern(2*mu-x,data)+1e-100)
p1*p2
}
pln=function(mu,data)
{
h=dpik(data,kernel="normal")
out=integrate(dfint, lower=min(data)-10*h,
upper=max(data)+10*h, data=data, mu=mu)
-out$value
}
hatmu.smooth=function(data)
{
##Input: data set
##Output: maximum smoothed likelihood estimate
hl.est=hl.loc(data)
est=optim(hl.est,pln,data=data,method="BFGS")$par
est
}
##Here is an example
set.seed(1221)
data=rnorm(100,0,1)
hatmu.smooth(data)
##Result: 0.1798129
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this chapter
Cite this chapter
Li, P., Tian, Z. (2016). Maximum Smoothed Likelihood Estimation of the Centre of a Symmetric Distribution. In: Chen, DG., Chen, J., Lu, X., Yi, G., Yu, H. (eds) Advanced Statistical Methods in Data Science. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-2594-5_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-2594-5_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2593-8
Online ISBN: 978-981-10-2594-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)