scholarly journals Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling

Biometrika ◽  
2011 ◽  
Vol 98 (1) ◽  
pp. 107-118 ◽  
Author(s):  
H. Cardot ◽  
E. Josserand
Keyword(s):  
Functional Data ◽  
Optimal Allocation ◽  
Stratified Sampling ◽  
Confidence Bands

scholarly journals Estimation of Sensitive Attributes Using a Stratified Kuk Randomization Device

2017 ◽  
Vol 40 (1) ◽  
pp. 29-44
Author(s):  
Jong-Min Kim ◽  
Gi-Sung Lee ◽  
Ki-Hak Hong ◽  
Chang-Kyoon Son
Keyword(s):  
Optimal Allocation ◽  
Stratified Sampling ◽  
Double Sampling ◽  
Proposed Model ◽  
Sensitive Parameters ◽  
Allocation Methods

This paper suggests a stratified Kuk model to estimate the proportion of sensitive attributes of a population composed by a number of strata; this is undertaken  by applying stratified sampling to the adjusted Kuk model. The paper estimates sensitive parameters when the size of the stratum is known by taking proportional and optimal allocation methods into account and then extends to the case of an unknown stratum size, estimating sensitive parameters by applying stratified double sampling and checking the two allocation methods. Finally, the paper compares the efficiency of the proposed model to that of the Su, Sedory  and Singh model and the adjusted Kuk model in terms of the estimator variance.

scholarly journals Resampling methods for functional data

2018 ◽  
Author(s):  
Timothy McMurry ◽  
Dimitris Politis
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Nonparametric Regression ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Real Data ◽  
Confidence Bands ◽  
Resampling Methods ◽  
Current State ◽  
Inference Techniques

This article examines the current state of methodological and practical developments for resampling inference techniques in functional data analysis, paying special attention to situations where either the data and/or the parameters being estimated take values in a space of functions. It first provides the basic background and notation before discussing bootstrap results from nonparametric smoothing, taking into account confidence bands in density estimation as well as confidence bands in nonparametric regression and autoregression. It then considers the major results in subsampling and what is known about bootstraps, along with a few recent real-data applications of bootstrapping with functional data. Finally, it highlights possible directions for further research and exploration.

scholarly journals Optimal allocation of stratified sampling design using Gradient Projection method

2017 ◽  
Vol 10 (1) ◽  
pp. 11-17
Author(s):  
M. A Lone ◽  
S. A Mir ◽  
Imran Khan ◽  
M. S Wani
Keyword(s):  
Projection Method ◽  
Iterative Procedure ◽  
Sampling Design ◽  
Optimal Allocation ◽  
Stratified Sampling ◽  
Gradient Projection ◽  
Integer Solution ◽  
Gradient Projection Method ◽  
Non Linear Programming ◽  
The Cost

This article deals with the problem of finding an optimal allocation of sample sizes in stratified sampling design to minimize the cost function. In this paper the iterative procedure of Rosen’s Gradient projection method is used to solve the Non linear programming problem (NLPP), when a non integer solution is obtained after solving the NLPP then Branch and Bound method provides an integer solution.

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