Nonparametric Methods in Survey Sampling

2005 ◽  
pp. 203-210 ◽  
Author(s):  
Giorgio E. Montanari ◽  
M. Giovanna Ranalli
Keyword(s):  
Nonparametric Methods ◽  
Survey Sampling

Assessing dependence changes using nonparametric methods

2007 ◽  
Vol 3 (6) ◽  
pp. 397-401 ◽  
Author(s):  
Param Silvapulle ◽  
Xibin Zhang
Keyword(s):  
Nonparametric Methods

scholarly journals On the Estimation of Reliability of Weighted Weibull Distribution: A Comparative Study

2016 ◽  
Vol 5 (4) ◽  
pp. 1
Author(s):  
Bander Al-Zahrani
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Real Data ◽  
Reliability Function ◽  
Nonparametric Methods ◽  
Empirical Method ◽  
Density Estimator ◽  
Bayes Estimators ◽  
Unknown Parameters ◽  
Data Set

The paper gives a description of estimation for the reliability function of weighted Weibull distribution. The maximum likelihood estimators for the unknown parameters are obtained. Nonparametric methods such as empirical method, kernel density estimator and a modified shrinkage estimator are provided. The Markov chain Monte Carlo method is used to compute the Bayes estimators assuming gamma and Jeffrey priors. The performance of the maximum likelihood, nonparametric methods and Bayesian estimators is assessed through a real data set.

scholarly journals Analysis of high level ozone concentrations using nonparametric methods

2011 ◽  
Vol 409 (6) ◽  
pp. 1123-1133 ◽  
Author(s):  
Alejandro Quintela-del-Río ◽  
Mario Francisco-Fernández
Keyword(s):  
Nonparametric Methods ◽  
Ozone Concentrations ◽  
High Level

scholarly journals Nonparametric Density and Regression Estimation

2001 ◽  
Vol 15 (4) ◽  
pp. 11-28 ◽  
Author(s):  
John DiNardo ◽  
Justin L Tobias
Keyword(s):  
Functional Form ◽  
Regression Function ◽  
Nonparametric Methods ◽  
Actual Data ◽  
Attractive Alternative ◽  
Regression Estimation ◽  
Parametric Methods ◽  
Regression Functions

We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to “simpler” parametric methods.

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