Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics

2006 ◽  
Vol 30 (8) ◽  
pp. 652-665 ◽  
Author(s):  
Jeesun Jung ◽  
Daniel E. Weeks ◽  
Eleanor Feingold
Keyword(s):  
Variance Estimation ◽  
Allele Sharing ◽  
Empirical Variance

NOISE VARIANCE ESTIMATION IN MAGNITUDE MAGNETIC RESONANCE IMAGES

2020 ◽  
Vol 9 (6) ◽  
pp. 3855-3863
Author(s):  
R. Singh ◽  
L. Kaur
Keyword(s):  
Magnetic Resonance ◽  
Variance Estimation ◽  
Magnetic Resonance Images ◽  
Noise Variance

A Note on Bootstrap Variance Estimation

1988 ◽  
Author(s):  
Jun Shao
Keyword(s):  
Variance Estimation

GMM Long-Run Variance Estimation via VAR Averaging

2018 ◽  
Author(s):  
Jen-Che Liao
Keyword(s):  
Variance Estimation ◽  
Long Run

scholarly journals Joint adjustment by CPIII precision trigonometric elevation control network assisted with precision levelling observation

2020 ◽  
Vol 165 ◽  
pp. 03005
Author(s):  
Li Jianzhang
Keyword(s):  
Variance Estimation ◽  
Estimation Method ◽  
Parameter Method ◽  
Control Network ◽  
Network Construction

Using the precision trigonometric elevation instead of the precision levelling to build a CPⅢ elevation control network will greatly increase the speed of CPⅢ control network construction. However, the accuracy of CPIII precision trigonometric elevation control network is still difficult to reach the level of CPⅢ precision levelling network. Based on the existing parameter method, this paper introduces some precision levelling for joint adjustment, and uses Helmert’s variance estimation method to perform strict weight determination. Our experiments show that when the number of precision levelling participating in the joint adjustment exceeds 1/3 of the total number of CPⅢ precision levelling network observations, the accuracy of the CPIII precision trigonometric elevation control network can be effectively improved.

scholarly journals An approximate point-based alternative for the estimation of variance under big BAF sampling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H. Gove ◽  
Timothy G. Gregoire ◽  
Mark J. Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Volume Estimation ◽  
Delta Method ◽  
Sampling Point ◽  
Variance Estimator ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula. Results Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of $\frac {1}{n}$ 1 n where n is the number of sample points.

Accuracy of Model-based and Learning-based Approaches for Image Noise Variance Estimation

2020 ◽  
Author(s):  
Mykhail L. Uss ◽  
Vladimir V. Lukin ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Variance Estimation ◽  
Image Noise ◽  
Noise Variance ◽  
Model Based

scholarly journals Food Environments and Hepatocellular Carcinoma Incidence

2021 ◽  
Vol 18 (11) ◽  
pp. 5740
Author(s):  
Mimi Ton ◽  
Michael J. Widener ◽  
Peter James ◽  
Trang VoPham
Keyword(s):  
Hepatocellular Carcinoma ◽  
Food Environment ◽  
Variance Estimation ◽  
Cancer Registries ◽  
County Level ◽  
Individual Level ◽  
Retail Food Environment ◽  
Food Retailers ◽  
Environment Index ◽  
Rate Ratios

Research into the potential impact of the food environment on liver cancer incidence has been limited, though there is evidence showing that specific foods and nutrients may be potential risk or preventive factors. Data on hepatocellular carcinoma (HCC) cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries. The county-level food environment was assessed using the Modified Retail Food Environment Index (mRFEI), a continuous score that measures the number of healthy and less healthy food retailers within counties. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between mRFEI scores and HCC risk, adjusting for individual- and county-level factors. The county-level food environment was not associated with HCC risk after adjustment for individual-level age at diagnosis, sex, race/ethnicity, year, and SEER registry and county-level measures for health conditions, lifestyle factors, and socioeconomic status (adjusted IRR: 0.99, 95% CI: 0.96, 1.01). The county-level food environment, measured using mRFEI scores, was not associated with HCC risk.

Linearization and variance estimation of the Bonferroni inequality index

2021 ◽  
Author(s):  
Ziqing Dong ◽  
Yves Tillé ◽  
Giovanni M. Giorgi ◽  
Alessio Guandalini
Keyword(s):  
Variance Estimation ◽  
Inequality Index ◽  
Bonferroni Inequality

Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation

2021 ◽  
pp. 1-1
Author(s):  
Ming-Wei Wu ◽  
Yan Jin ◽  
Yan Li ◽  
Tianyu Song ◽  
Pooi Yuen Kam
Keyword(s):  
Maximum Likelihood ◽  
Variance Estimation ◽  
Noise Variance
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