Pitman closeness properties of point estimators and predictive densities with parametric constraints

2016 ◽  
Vol 116 ◽  
pp. 101-106 ◽  
Author(s):  
Takeru Matsuda ◽  
William E. Strawderman
Keyword(s):  
Pitman Closeness ◽  
Point Estimators ◽  
Predictive Densities

Location Properties of Point Estimators in Linear Instrumental Variables and Related Models

2014 ◽  
Vol 34 (6-10) ◽  
pp. 720-733 ◽  
Author(s):  
Keisuke Hirano ◽  
Jack R. Porter
Keyword(s):  
Instrumental Variables ◽  
Point Estimators

scholarly journals Comparison of Variance Estimators for Systematic Environmental Sample Surveys: Considerations for Post-Stratified Estimation

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 772
Author(s):  
Bryce Frank ◽  
Vicente J. Monleon
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
National Forest Inventory ◽  
Point Estimation ◽  
Systematic Sampling ◽  
Sampling Variance ◽  
Variance Estimators ◽  
The Past ◽  
Point Estimators ◽  
Species Specific

The estimation of the sampling variance of point estimators under two-dimensional systematic sampling designs remains a challenge, and several alternative variance estimators have been proposed in the past few decades. In this work, we compared six alternative variance estimators under Horvitz-Thompson (HT) and post-stratification (PS) point estimation regimes. We subsampled a multitude of species-specific forest attributes from a large, spatially balanced national forest inventory to compare the variance estimators. A variance estimator that assumes a simple random sampling design exhibited positive relative bias under both HT and PS point estimation regimes ranging between 1.23 to 1.88 and 1.11 to 1.78 for HT and PS, respectively. Alternative estimators reduced this positive bias with relative biases ranging between 1.01 to 1.66 and 0.90 to 1.64 for HT and PS, respectively. The alternative estimators generally obtained improved efficiencies under both HT and PS, with relative efficiency values ranging between 0.68 to 1.28 and 0.68 to 1.39, respectively. We identified two estimators as promising alternatives that provide clear improvements over the simple random sampling estimator for a wide variety of attributes and under HT and PS estimation regimes.

Pitman closeness for Stein-rule estimators of regression coefficients

1993 ◽  
Vol 18 (2) ◽  
pp. 85-89 ◽  
Author(s):  
A.K. Srivastava ◽  
V.K. Srivastava
Keyword(s):  
Regression Coefficients ◽  
Pitman Closeness

Asymptotic properties of stereological estimators of volume fraction for stationary random sets

1982 ◽  
Vol 19 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Shigeru Mase
Keyword(s):  
Volume Fraction ◽  
Asymptotic Variance ◽  
Asymptotic Properties ◽  
Parametric Estimation ◽  
Random Sets ◽  
Stationary Time Series ◽  
Boolean Models ◽  
Window Functions ◽  
Spectral Density Functions ◽  
Point Estimators

We shall discuss asymptotic properties of stereological estimators of volume (area) fraction for stationary random sets (in the sense of Matheron) under natural and general assumptions. Results obtained are strong consistency, asymptotic normality, and asymptotic unbiasedness and consistency of asymptotic variance estimators. The method is analogous to the non-parametric estimation of spectral density functions of stationary time series using window functions. Proofs are given for areal estimators, but they are also valid for lineal and point estimators with slight modifications. Finally we show that stationary Boolean models satisfy the relevant assumptions reasonably well.

Small-Sample Bias of Point Estimators of the Odds Ratio from Matched Sets

Biometrics ◽  
1984 ◽  
Vol 40 (2) ◽  
pp. 421 ◽  
Author(s):  
Nicholas P. Jewell
Keyword(s):  
Odds Ratio ◽  
Small Sample ◽  
Sample Bias ◽  
Point Estimators

scholarly journals Performance of the GraybillnDeal Estimator via Pitman Closeness Criterion

2018 ◽  
Vol 17 (2) ◽  
pp. 291
Author(s):  
Keyu Nie ◽  
Bikas K. Sinha ◽  
A. S Hedayat
Keyword(s):  
Pitman Closeness ◽  
Pitman Closeness Criterion

Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities

2002 ◽  
Vol 14 (10) ◽  
pp. 2439-2468 ◽  
Author(s):  
Aki Vehtari ◽  
Jouko Lampinen
Keyword(s):  
Expected Utility ◽  
Cross Validation ◽  
Monte Carlo Sampling ◽  
Point Estimate ◽  
Model Assessment ◽  
Predictive Densities ◽  
Fold Cross Validation ◽  
Selection Of ◽  
Natural Way ◽  
Real World Problems

In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate because it describes the uncertainty in the estimate. The distributions of the expected utility estimates can also be used to compare models, for example, by computing the probability of one model having a better expected utility than some other model. We propose an approach using cross-validation predictive densities to obtain expected utility estimates and Bayesian bootstrap to obtain samples from their distributions. We also discuss the probabilistic assumptions made and properties of two practical cross-validation methods, importance sampling and k-fold cross-validation. As illustrative examples, we use multilayer perceptron neural networks and gaussian processes with Markov chain Monte Carlo sampling in one toy problem and two challenging real-world problems.

Unbiased point estimators

1980 ◽  
pp. 23-51
Author(s):  
G. P. Beaumont
Keyword(s):  
Point Estimators

Point Estimators

2011 ◽  
pp. 413-462
Keyword(s):  
Point Estimators
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