Статті в журналах: "Variance components testing"

1

Li, Zaixing, Fei Chen, and Lixing Zhu. "Variance Components Testing inANOVA-Type Mixed Models." Scandinavian Journal of Statistics 41, no. 2 (November 20, 2013): 482–96. http://dx.doi.org/10.1111/sjos.12044.

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2

Johansson, Anders S., Thomas B. Whitaker, Winston M. Hagler, Francis G. Giesbrecht, James H. Young, and Daryl T. Bowman. "Testing Shelled Corn for Aflatoxin, Part I: Estimation of Variance Components." Journal of AOAC INTERNATIONAL 83, no. 5 (September 1, 2000): 1264–69. http://dx.doi.org/10.1093/jaoac/83.5.1264.

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Abstract The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1%), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.

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3

Weerahandi, Samaradasa. "Testing Variance Components in Mixed Models with GeneralizedpValues." Journal of the American Statistical Association 86, no. 413 (March 1991): 151–53. http://dx.doi.org/10.1080/01621459.1991.10475013.

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4

Vargas, Eugenia A., Thomas B. Whitaker, Eliene A. Santos, Andrew B. Slate, Francisco B. Lima, and Regina C. A. França. "Testing Green Coffee for Ochratoxin A, Part I: Estimation of Variance Components." Journal of AOAC INTERNATIONAL 87, no. 4 (July 1, 2004): 884–91. http://dx.doi.org/10.1093/jaoac/87.4.884.

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Abstract The variability associated with testing lots of green coffee beans for ochratoxin A (OTA) was investigated. Twenty-five lots of green coffee were tested for OTA contamination. The total variance associated with testing green coffee was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased with an increase in OTA concentration. Using regression analysis, mathematical expressions were developed to model the relationship between OTA concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific OTA concentration. Testing a lot with 5 μg/kg OTA using a 1 kg sample, Romer RAS mill, 25 g subsamples, and liquid chromatography analysis, the total, sampling, sample preparation, and analytical variances were 10.75 (coefficient of variation [CV] = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively. The total variance for sampling, sample preparation, and analytical were 73, 26, and 1%, respectively.

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5

Stram, Daniel O., and Jae Won Lee. "Variance Components Testing in the Longitudinal Mixed Effects Model." Biometrics 50, no. 4 (December 1994): 1171. http://dx.doi.org/10.2307/2533455.

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6

Michalski, Andrzej, and Roman Zmyślony. "Testing Hypotheses for Variance Components in Mixed Linear Models." Statistics 27, no. 3-4 (January 1996): 297–310. http://dx.doi.org/10.1080/02331889708802533.

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7

Sirkova, Lydia, and Viktor Witkovsky. "On Testing Variance Components in Unbalanced Mixed Linear Model." Applications of Mathematics 46, no. 3 (June 2001): 191–213. http://dx.doi.org/10.1023/a:1013739907119.

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8

Du, Han, and Lijuan Wang. "Testing Variance Components in Linear Mixed Modeling Using Permutation." Multivariate Behavioral Research 55, no. 1 (June 27, 2019): 120–36. http://dx.doi.org/10.1080/00273171.2019.1627513.

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9

Drikvandi, R., G. Verbeke, A. Khodadadi, and V. Partovi Nia. "Testing multiple variance components in linear mixed-effects models." Biostatistics 14, no. 1 (August 28, 2012): 144–59. http://dx.doi.org/10.1093/biostatistics/kxs028.

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10

Drikvandi, Reza, Ahmad Khodadadi, and Geert Verbeke. "Testing variance components in balanced linear growth curve models." Journal of Applied Statistics 39, no. 3 (March 2012): 563–72. http://dx.doi.org/10.1080/02664763.2011.603294.

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11

Weerahandi, Samaradasa. "Testing Variance Components in Mixed Models With Generalized p Values." Journal of the American Statistical Association 86, no. 413 (March 1991): 151. http://dx.doi.org/10.2307/2289724.

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12

Lindquist, Martin A., Julie Spicer, Iris Asllani, and Tor D. Wager. "Estimating and testing variance components in a multi-level GLM." NeuroImage 59, no. 1 (January 2012): 490–501. http://dx.doi.org/10.1016/j.neuroimage.2011.07.077.

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13

Li, Xinmin, and Juan Wang. "A Simple Method for Testing Variance Components in Unbalanced Nested Model." Communications in Statistics - Simulation and Computation 40, no. 9 (October 2011): 1310–23. http://dx.doi.org/10.1080/03610918.2011.569861.

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14

RAMIREZ-MARQUEZ, JOSE E., DAVID W. COIT, and TONGDAN JIN. "TEST PLAN ALLOCATION TO MINIMIZE SYSTEM RELIABILITY ESTIMATION VARIABILITY." International Journal of Reliability, Quality and Safety Engineering 11, no. 03 (September 2004): 257–72. http://dx.doi.org/10.1142/s0218539304001506.

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A new methodology is presented to allocate testing units to the different components within a system when the system configuration is fixed and there are budgetary constraints limiting the amount of testing. The objective is to allocate additional testing units so that the variance of the system reliability estimate, at the conclusion of testing, will be minimized. Testing at the component-level decreases the variance of the component reliability estimate, which then decreases the system reliability estimate variance. The difficulty is to decide which components to test given the system-level implications of component reliability estimation. The results are enlightening because the components that most directly affect the system reliability estimation variance are often not those components with the highest initial uncertainty. The approach presented here can be applied to any system structure that can be decomposed into a series-parallel or parallel-series system with independent component reliability estimates. It is demonstrated using a series-parallel system as an example. The planned testing is to be allocated and conducted iteratively in distinct sequential testing runs so that the component and system reliability estimates improve as the overall testing progresses. For each run, a nonlinear programming problem must be solved based on the results of all previous runs. The testing allocation process is demonstrated on two examples.

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15

Greven, Sonja, Ciprian M. Crainiceanu, Helmut Küchenhoff, and Annette Peters. "Restricted Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models." Journal of Computational and Graphical Statistics 17, no. 4 (December 2008): 870–91. http://dx.doi.org/10.1198/106186008x386599.

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16

Novick, Steven, and Buffy Hudson-Curtis. "Efficient Computing for One and Two Variance Components Parametric Tolerance Interval Testing." Statistics in Biopharmaceutical Research 11, no. 2 (April 3, 2019): 146–51. http://dx.doi.org/10.1080/19466315.2018.1447994.

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17

Gupta, Arjun K., Solomon W. Harrar, and Yasunori Fujikoshi. "Asymptotics for testing hypothesis in some multivariate variance components model under non-normality." Journal of Multivariate Analysis 97, no. 1 (January 2006): 148–78. http://dx.doi.org/10.1016/j.jmva.2004.12.001.

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18

Visscher, Peter M. "A Note on the Asymptotic Distribution of Likelihood Ratio Tests to Test Variance Components." Twin Research and Human Genetics 9, no. 4 (August 1, 2006): 490–95. http://dx.doi.org/10.1375/twin.9.4.490.

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AbstractWhen using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a χ2 distribution with degrees of freedom equal to the number of parameters tested. Using the relationship between least squares and maximum likelihood estimators for balanced designs, it is shown why the asymptotic distribution of the likelihood ratio test for variance components does not follow a χ2 distribution with degrees of freedom equal to the number of parameters tested when the null hypothesis is true. Instead, the distribution of the likelihood ratio test is a mixture of χ2 distributions with different degrees of freedom. Implications for testing variance components in twin designs and for quantitative trait loci mapping are discussed. The appropriate distribution of the likelihood ratio test statistic should be used in hypothesis testing and model selection.

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19

Whitaker, T. B., J. W. Dorner, F. E. Dowell, and F. G. Giesbrecht. "Variability Associated with Chemically Testing Screened Farmers Stock Peanuts For Aflatoxin1." Peanut Science 19, no. 2 (July 1, 1992): 88–91. http://dx.doi.org/10.3146/i0095-3679-19-2-6.

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Abstract Forty farmers stock lots of runner peanuts suspected of containing aflatoxin were identified by the Federal State Inspection Service using the visual Aspergillus flavus method. A 227-kg portion was removed from each of the 40 lots. Each 227-kg portion was screened over a belt screening device with 0.953-cm (24/64 inch) spacing to remove loose shelled kernels, foreign material, and small pods. Each screened portion was divided into ten 9.5-kg samples. Each sample was shelled, all kernels in the sample were comminuted in the Federal State subsampling mill, and the aflatoxin in duplicate 356-g subsamples per sample was extracted and quantified using HPLC methods. The total variability among the 10 aflatoxin test results was determined for each lot. The total variability was partitioned into sampling, subsampling, and analytical variability components for each lot. All variance components were shown to be functions of the aflatoxin concentration. Using regression techniques the functional relationship for each variance components and aflatoxin concentration was developed. The total variance associated with a 9.5-kg sample, 356-g subsample, and HPLC quantification when testing a screened farmers stock lot at 20 ppb is 295.2 and the CV is 89.5%.

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20

Potts, David, Scott Hildreth, and Binod Kumar G. Nair. "Impacts of Nested Variance Components on Semiconductor Electrical Test Sampling." EDFA Technical Articles 24, no. 3 (August 1, 2022): 4–10. http://dx.doi.org/10.31399/asm.edfa.2022-3.p004.

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Abstract Inline wafer electrical testing (WET) offers an early read on semiconductor manufacturing processes via measurements taken on test structures placed throughout the wafer. Interpreting the data can be challenging, however. In many cases, only a sample of the test sites are monitored in production. Complex manufacturing requirements further complicate the problem because some operations are iteratively executed within subregions across a given wafer, while others are run on the entire wafer at once, and still others are applied to wafers in batches. This results in a nested variance structure under which different physical mechanisms exhibit varying sensitivities to site-to-site, wafer-to-wafer, and lot-to-lot variations. This article uses Monte Carlo simulations to explore the impacts these hierarchical variance components can exert on perceptions of WET performance.

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21

Ozer, H., H. I. Oktay Basegmez, T. B. Whitaker, A. B. Slate, and F. G. Giesbrecht. "Sampling dried figs for aflatoxin – Part 1: variability associated with sampling, sample preparation, and analysis." World Mycotoxin Journal 10, no. 1 (February 27, 2017): 31–40. http://dx.doi.org/10.3920/wmj2016.2052.

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The variability associated with the aflatoxin test procedure used to estimate aflatoxins in bulk shipments of dried figs was investigated. Sixteen 10 kg laboratory samples were taken from each of twenty commercial bulk lots of dried figs suspected of aflatoxin contamination. Two 55 g test portions were taken from each comminuted laboratory sample using water-slurry comminution methods. Finally, two aliquots from the test portion/solvent blend were analysed for both aflatoxin B1 and total aflatoxins. The total variance associated with testing dried figs for aflatoxins was measured and partitioned into sampling, sample preparation and analytical variance components (total variance is equal to the sum of the sampling variance, sample preparation variance, and analytical variance). Each variance component increased as aflatoxin concentration increased. Using regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation and analytical variances when testing dried figs for aflatoxins. The regression equations were modified to estimate the variances for any sample size, test portion size, and number of analyses for a specific lot aflatoxin concentration. When using the above aflatoxin test procedure to sample a fig lot at 10 μg/kg total aflatoxins, the sampling, sample preparation, analytical, and total variances were 47.20, 0.29, 0.13, and 47.62, respectively. The sampling, sample preparation, and analytical steps accounted for 99.1, 0.6, and 0.3% of the total variance, respectively. For the aflatoxin test procedure used in this study, the sampling step is the largest source of variability.

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22

Whitaker, Thomas B., Floyd E. Dowell, Winston M. Hagler, Francis G. Giesbrecht, and Jeremy Wu. "Variability Associated with Sampling, Sample Preparation, and Chemical Testing for Aflatoxin in Farmers' Stock Peanuts." Journal of AOAC INTERNATIONAL 77, no. 1 (January 1, 1994): 107–16. http://dx.doi.org/10.1093/jaoac/77.1.107.

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Abstract Forty farmers’ stock lots of runner peanuts suspected of containing aflatoxin were identified by the Federal State Inspection Service by using the visual Aspergillus flavus inspection method. A 900 kg portion was removed from each lot and divided into 50 samples each of 2.27 kg (5 lb), 4.54 kg (10 lb), and 6.81 kg (15 lb) weights. For each sample, foreign material was removed, pods were shelled, and all kernels were comminuted for 7 min in a vertical cutter mixer. A100 g subsample was removed from each comminuted sample for aflatoxin analysis by liquid chromatography (LC). The total variance associated with each sample size was estimated. The total variance was also partitioned into sampling, sample preparation, and analytical variance components. Each variance component was shown to be a function of aflatoxin concentration. By using regression techniques, the relationship between variance and aflatoxin concentration was developed for each variance component. The total, sampling, sample preparation, and analytical variances associated with testing a lot at 100 ppb with a 2.27 kg sample, 100 g subsample, and using LC analytical techniques are 25 378,23 533,1830, and 15, respectively. Sampling, sample preparation, and analysis account for 92.7, 7.2, and 0.1% of the total variability, respectively.

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23

Patriota, Alexandre Galvão, and Jônatas de Oliveira Alves. "A monotone frequentist measure of evidence for testing variance components in linear mixed models." Journal of Statistical Planning and Inference 219 (July 2022): 43–62. http://dx.doi.org/10.1016/j.jspi.2021.11.002.

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24

Li, Xinmin, Haiyan Su, and Hua Liang. "Fiducial generalized p-values for testing zero-variance components in linear mixed-effects models." Science China Mathematics 61, no. 7 (April 4, 2018): 1303–18. http://dx.doi.org/10.1007/s11425-016-9068-8.

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25

Gelber, David A., Michael A. Pfeifer, Vasti L. Broadstone, Edward W. Munster, Michael Peterson, Joseph C. Arezzo, Harry Shamoon, et al. "Components of variance for vibratory and thermal threshold testing in normal and diabetic subjects." Journal of Diabetes and its Complications 9, no. 3 (July 1995): 170–76. http://dx.doi.org/10.1016/1056-8727(94)00042-m.

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26

Li, Zhen, Junfeng Tian, and Pengyuan Zhao. "Software Reliability Estimate with Duplicated Components Based on Connection Structure." Cybernetics and Information Technologies 14, no. 3 (September 1, 2014): 3–13. http://dx.doi.org/10.2478/cait-2014-0028.

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Abstract Reliability testing of complex software at the system level is impossible due to the environmental constraint or the time limitation, so its reliability estimate is often obtained based on the reliability of subsystems or components. The connection structure was defined and the component-based software reliability was estimated based on it. For the present popular software with duplicated components, an approach to variance estimation of software reliability for complex structure systems was proposed, which has improved the hierarchical decomposition approach of variance estimation just for series-parallel systems. Experimental results indicated that the approach to variance estimation for reliability of software with duplicated components has advantages, such as the simple calculation process, small error result, and suitability for complex structure systems. Finally, the sensitivity analysis, used to identify critical components for resource allocation, could better improve the software reliability

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27

Steele, Bernard W., Edward Wang, Glenn Palomaki, George G. Klee, Ronald J. Elin, and David L. Witte. "Sources of Variability." Archives of Pathology & Laboratory Medicine 125, no. 2 (February 1, 2001): 183–90. http://dx.doi.org/10.5858/2001-125-0183-sov.

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Abstract Objective.—To determine the magnitudes and sources of analytic variation in testing for therapeutic drugs. Specifically, among laboratories using the same analytic method, to compare the within-laboratory variation (including both short- and long-term variation) with the between-laboratory variation. Design.—Four identical challenges were prepared from a lyophilized pool of spiked sera and were sent in pairs 4 months apart to laboratories participating in a nationwide proficiency-testing program. For each of 25 drugs, the variability in reported results from laboratories using the same method was investigated using nested analysis of variance. Setting.—The first 2 mailings of the College of American Pathologists Therapeutic Drug Monitoring Survey, 1996, sets Z and ZM. Main Outcome Measures.—For each drug, total variance was partitioned into within- and between-laboratory components for common methods. The within-laboratory component was further partitioned into short- and long-term components. Participants.—The approximately 5000 laboratories enrolled in the survey. Results.—For the 25 drugs, the average percentages of the total variance due to short-term, within-laboratory variance; long-term, within-laboratory variance; between-laboratory variance; and total laboratory variance were 25.0% (range, 8.8–50.6%), 57.8% (35.3–73.7%), 17.3% (5.0–35.4%), and 82.7% (64.6–95.0%), respectively. Conclusion.—For all drugs tested, the within-laboratory component of variance was greater than the between-laboratory component of variance. Within laboratories, the magnitude of the long-term component was generally greater than the magnitude of the short-term component. This information will be helpful in determining the clinical utility of various drug assays and in evaluating the appropriateness of regulations involving therapeutic drug testing.

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28

Wang, Yuanjia, and Huaihou Chen. "On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components." Biometrics 68, no. 4 (September 28, 2012): 1113–25. http://dx.doi.org/10.1111/j.1541-0420.2012.01790.x.

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29

Lindquist, M. A., J. Spicer, L. Leotti, I. Asllani, and T. D. Wager. "Localizing areas with significant inter-individual variation: Testing Variance Components in a Multi-level GLM." NeuroImage 47 (July 2009): S125. http://dx.doi.org/10.1016/s1053-8119(09)71204-2.

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30

Kongsuwannakul, Kunlaphak. "A Case of the Validity Investigation of Concordance-based Cloze Testing: Construct Relevance Revisited." Suranaree Journal of Social Science 14, no. 1 (June 8, 2020): 117–28. http://dx.doi.org/10.55766/vhpy3680.

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Investigating construct validity is a process that is essential especially for devising a new item type. However, the distinction between construct-relevant and construct-irrelevant variances may not always be sufficient. In this article, the construct of the concordance-based cloze item type (henceforth ConCloze) will be defined through two research projects. The first one is the doctoral dissertation (Kongsuwannakul, 2017 Investigating the Construct Validity of a Concordance-based Cloze Test: A Mixed-methods Study), in which the construct validity of the item type is defined with an iterative research design. Item components and a variety of changes to them are used insofar as information about the language processes and domains is obtained out of the data. The other research project is a follow-up study (Kongsuwannakul, 2019 Suranaree University of Technology Students’ Language Domains in Engaging with a Concordance-based Cloze Test: A Contrastive Approach), in which a contrastive approach is used for identifying distinguishing language domains in ConCloze. It is found that language domains such as knowledge of lexical semantics are not sufficiently distinguishing the construct of the item type. As for theoretical implications, it will be argued that construct-relevant variance could be divided into construct variance and construct-peripheral variance.

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31

Morrell, Christopher H. "Likelihood Ratio Testing of Variance Components in the Linear Mixed-Effects Model Using Restricted Maximum Likelihood." Biometrics 54, no. 4 (December 1998): 1560. http://dx.doi.org/10.2307/2533680.

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32

de Silva, H. N., and I. L. Gordon. "Variance components in safflower (Carthamus tinctorius) trials in the Manawatu district, with implications for testing methods." New Zealand Journal of Experimental Agriculture 14, no. 1 (January 1986): 1–6. http://dx.doi.org/10.1080/03015521.1986.10426116.

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33

Anderson, T. W. "The asymptotic distribution of the likelihood ratio criterion for testing rank in multivariate components of variance." Journal of Multivariate Analysis 30, no. 1 (July 1989): 72–79. http://dx.doi.org/10.1016/0047-259x(89)90088-2.

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34

Xi Chen, Song, and Jiti Gao. "SIMULTANEOUS SPECIFICATION TESTING OF MEAN AND VARIANCE STRUCTURES IN NONLINEAR TIME SERIES REGRESSION." Econometric Theory 27, no. 4 (March 3, 2011): 792–843. http://dx.doi.org/10.1017/s0266466610000502.

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This paper proposes a nonparametric simultaneous test for parametric specification of the conditional mean and variance functions in a time series regression model. The test is based on an empirical likelihood (EL) statistic that measures the goodness of fit between the parametric estimates and the nonparametric kernel estimates of the mean and variance functions. A unique feature of the test is its ability to distribute natural weights automatically between the mean and the variance components of the goodness-of-fit measure. To reduce the dependence of the test on a single pair of smoothing bandwidths, we construct an adaptive test by maximizing a standardized version of the empirical likelihood test statistic over a set of smoothing bandwidths. The test procedure is based on a bootstrap calibration to the distribution of the empirical likelihood test statistic. We demonstrate that the empirical likelihood test is able to distinguish local alternatives that are different from the null hypothesis at an optimal rate.

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35

Fišerová, Eva, and Lubomír Kubáček. "Sensitivity Analysis for the Decomposition of Mixed Partitioned Multivariate Models into Two Seemingly Unrelated Submodels." Austrian Journal of Statistics 43, no. 3 (June 12, 2014): 167–79. http://dx.doi.org/10.17713/ajs.v43i3.29.

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The paper is focused on the decomposition of mixed partitioned multivariate models into two seemingly unrelated submodels in order to obtain more efficient estimators. The multiresponses are independently normally distributed with the same covariance matrix. The partitioned multivariate model is considered either with, or without an intercept. The elimination transformation of the intercept that preserves the BLUEs of parameter matri- ces and the MINQUE of the variance components in multivariate models with and without an intercept is stated. Procedures on testing the decomposition of the partitioned model are presented. The properties of plug-in test statistics as functions of variance compo- nents are investigated by sensitivity analysis and insensitivity regions for the significance level are proposed. The insensitivity region is a safe region in the parameter space of the variance components where the approximation of the variance components can be used without any essential deterioration of the significance level of the plug-in test statistic. The behavior of plug-in test statistics and insensitivity regions is studied by simulations.

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36

Cole, Eugene C., William A. Rutala, and Gregory P. Samsa. "Disinfectant Testing Using a Modified Use-Dilution Method: Collaborative Study." Journal of AOAC INTERNATIONAL 71, no. 6 (November 1, 1988): 1187–94. http://dx.doi.org/10.1093/jaoac/71.6.1187.

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Abstract An initial collaborative study of the AOAC use-dilution method (UDM), used for bactericidal disinfectant efficacy testing, demonstrated extreme variability of test results among the 18 laboratories testing identical hospital disinfectants. In an effort to improve the method, 32 changes were made by the UDM Task Force. These changes represented improvements in quality assurance practices and elimination of method variability; however, the basic framework of the method was retained. A second collaborative trial was conducted to determine if the interlaboratory variability of test results could be reduced to an acceptable level using the modified UDM. Twelve of the original 18 laboratories participated in the second study. Each laboratory processed 60 penicylinders (P) for each of the 6 randomly selected, federally registered disinfectants and 3 test organisms {Staphylococcus aureus, Salmonella choleraesuis, Pseudomonas aeruginosa). The number of positive penicylinders (>1 positive P/60 replicates = failure) for the 6 products when P. aeruginosa was used as the challenge organism ranged 1-30, 0-36, 0-15, 0-5, 0-3, and 0-60 for the 3 quaternaries and 3 phenolics, respectively. The results of the variance components analysis for P. aeruginosa and the other 2 organisms showed that the variance components for laboratories were not significantly reduced for any organism in this study. Such interlaboratory variability of results questions the use of the original or the modified UDM for registration purposes

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37

Li, Gengxin, and Yuehua Cui. "A Statistical Variance Components Framework for Mapping Imprinted Quantitative Trait Locus in Experimental Crosses." Journal of Probability and Statistics 2009 (2009): 1–27. http://dx.doi.org/10.1155/2009/689489.

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Current methods for mapping imprinted quantitative trait locus (iQTL) with inbred line crosses assume fixed QTL effects. When an iQTL segregates in experimental line crosses, combining different line crosses with similar genetic background can improve the accuracy of iQTLs inference. In this article, we develop a general interval-based statistical variance components framework to map iQTLs underlying complex traits by combining different backcross line crosses. We propose a new iQTL variance partition method based on the nature of marker alleles shared identical-by-decent (IBD) in inbred lines. Maternal effect is adjusted when testing imprinting. Efficient estimation methods with the maximum likelihood and the restricted maximum likelihood are derived and compared. Statistical properties of the proposed mapping strategy are evaluated through extensive simulations under different sampling designs. An extension to multiple QTL analysis is given. The proposed method will greatly facilitate genetic dissection of imprinted complex traits in inbred line crosses.

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38

Barnabani, Marco. "A Parametric Test to Discriminate Between a Linear Regression Model and a Linear Latent Growth Model." International Journal of Statistics and Probability 6, no. 3 (May 14, 2017): 157. http://dx.doi.org/10.5539/ijsp.v6n3p157.

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In longitudinal studies with subjects measured repeatedly across time, an important problem is how to select a model generating data by choosing between a linear regression model and a linear latent growth model. Approaches based both on information criteria and asymptotic hypothesis tests of the variances of ''random'' components are widely used but not completely satisfactory. We propose a test statistic based on the trace of the product of an estimate of a variance covariance matrix defined when data come from a linear regression model and a sample variance covariance matrix. We studied the sampling distribution of the test statistic giving a representation in terms of an infinite series of generalized F-distributions. Knowledge about this distribution allows us to make inference within a classical hypothesis testing ramework. The test statistic can be used by itself to discriminate between the two models and/or, if duly modified, it can be used to test randomness on single components. Moreover, in conjunction with some model selection criteria, it gives additional information which can help in choosing the model.

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39

Shete, Sanjay, and Christopher I. Amos. "Testing for Genetic Linkage in Families by a Variance-Components Approach in the Presence of Genomic Imprinting." American Journal of Human Genetics 70, no. 3 (March 2002): 751–57. http://dx.doi.org/10.1086/338931.

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40

Ozay, Guner, Ferda Seyhan, Aysun Yilmaz, Thomas B. Whitaker, Andrew B. Slate, and Francis Giesbrecht. "Sampling Hazelnuts for Aflatoxin: Uncertainty Associated with Sampling, Sample Preparation, and Analysis." Journal of AOAC INTERNATIONAL 89, no. 4 (July 1, 2006): 1004–11. http://dx.doi.org/10.1093/jaoac/89.4.1004.

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Анотація:

Abstract The variability associated with the aflatoxin test procedure used to estimate aflatoxin levels in bulk shipments of hazelnuts was investigated. Sixteen 10 kg samples of shelled hazelnuts were taken from each of 20 lots that were suspected of aflatoxin contamination. The total variance associated with testing shelled hazelnuts was estimated and partitioned into sampling, sample preparation, and analytical variance components. Each variance component increased as aflatoxin concentration (either B1 or total) increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. The sampling, sample preparation, and analytical variances associated with estimating aflatoxin in a hazelnut lot at a total aflatoxin level of 10 ng/g and using a 10 kg sample, a 50 g subsample, dry comminution with a Robot Coupe mill, and a highperformance liquid chromatographic analytical method are 174.40, 0.74, and 0.27, respectively. The sampling, sample preparation, and analytical steps of the aflatoxin test procedure accounted for 99.4, 0.4, and 0.2% of the total variability, respectively.

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41

Heath, S. C., G. Bulfield, R. Thompson, and P. D. Keightley. "Rates of change of genetic parameters of body weight in selected mouse lines." Genetical Research 66, no. 1 (August 1995): 19–25. http://dx.doi.org/10.1017/s0016672300034352.

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SummaryA method based on the animal model is described which allows the estimation of continuous changes in variance components over time using restricted maximum likelihood (REML). The method was applied to the analysis of a selection experiment in which a foundation population formed from a cross between two inbred strains of mice (C57BL/6J and DBA/2J) was divergently selected for 6 week body weight over 20 generations. The analysis suggested that there was an increase in phenotypic variance of about 50% in the low selected lines over the course of the experiment which was attributed to increases in the environmental and additive variance components. Variance changes in the High selected lines were generally smaller than in the Low lines, although there was an estimated 20% increase in the environmental variance. Simple models to explain these effects involving dominance, linkage and epistasis were explored. Testing which of these was responsible for the variance changes noted in this experiment (if any) is difficult, although the epistasis and dominance models require less stringent conditions than the linkage model, and the dominance model is supported by evidence of heterosis in the F1.

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42

Zhang, Yan, and Shi Sheng Zhou. "Research on Reconstruction of Spectral Reflectance Based on Principal Component Analysis." Applied Mechanics and Materials 262 (December 2012): 53–58. http://dx.doi.org/10.4028/www.scientific.net/amm.262.53.

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Traditional color reproduction technology based on the Metamerism principle, the disadvantage is that different observer condition leads to different color appearance.To fulfill the color consistency, the spectrum reflectance of the object color sample need to be reconstructed. The principal component analysis makes use of the linear combination of a few principal components to reconstruct the spectral reflectance of sample. This paper analyzes the 31*31 matrix of Munsell spectral data by the principle component analyze method and achieves the principal component for spectrum reflectance. The numbers of principal components are identified as six by discussing the variance contribution rate. Spectral reconstruction of four Munsell testing samples makes use of first six principal components, which has met the accuracy requirements. Research shows that the reconstruction of spectral accuracy decreased when training samples and testing samples belong to the different database.

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43

Fan, Yanqin, and Ramazan Gençay. "UNIT ROOT TESTS WITH WAVELETS." Econometric Theory 26, no. 5 (February 17, 2010): 1305–31. http://dx.doi.org/10.1017/s0266466609990594.

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This paper develops a wavelet (spectral) approach to testing the presence of a unit root in a stochastic process. The wavelet approach is appealing, since it is based directly on the different behavior of the spectra of a unit root process and that of a short memory stationary process. By decomposing the variance (energy) of the underlying process into the variance of its low frequency components and that of its high frequency components via the discrete wavelet transformation (DWT), we design unit root tests against near unit root alternatives. Since DWT is an energy preserving transformation and able to disbalance energy across high and low frequency components of a series, it is possible to isolate the most persistent component of a series in a small number of scaling coefficients. We demonstrate the size and power properties of our tests through Monte Carlo simulations.

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44

RADHIKA, K. R., S. V. SHEELA, and G. N. SEKHAR. "OFF-LINE SIGNATURE AUTHENTICATION USING RADIAL BASIS FUNCTION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 02 (March 2011): 207–25. http://dx.doi.org/10.1142/s0218001411008580.

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A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.

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45

Biage, Milton, and Pierre Joseph Nelcide. "Effects of asset frequency components on value-at-risk in emerging and developed markets." Brazilian Review of Econometrics 40, no. 1 (August 17, 2020): 145. http://dx.doi.org/10.12660/bre.v40n12020.77437.

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<p>Value-at-Risk was estimated using the technique of wavelet decomposition with goal to analyze the frequency components' impacts on variances of daily stock returns, and on forecasts. Daily returns of twenty-one shares of the Ibovespa and daily returns of twenty-two shares of the DJIA were used. The model was applied to the reconstructed returns to model and establish the prediction of conditional variance, applying the rolling window technique. The Value-at-Risk was then estimated, and the results showed that the DJIA shares showed more efficient market behavior than those of Ibovespa. The differences in behavior induces to affirm that VaRs, used in the analysis of financial assets from different markets with different governance premises, should be estimated by series of returns reconstructed by aggregations of components of different frequencies. A set of back-testing was applied to confront the estimated , which demonstrated that the estimation of models are consistent.</p>

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46

Basatini, Ferdous Mohammadi, and Saeid Rezakhah. "Smooth Transition HYGARCH Model: Stability and Testing." Fluctuation and Noise Letters 18, no. 04 (November 21, 2019): 1950025. http://dx.doi.org/10.1142/s0219477519500251.

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HYGARCH process is commonly used for modeling long memory volatility. Many financial time series are characterized by transition between different levels of volatilities. Smooth transition HYGARCH (ST-HYGARCH) model is proposed to model smooth transition between components of HYGARCH process. The behavior of the conditional variance in the ST-HYGARCH are allowed to change smoothly over time. The asymptotic finiteness of the second moment is studied. A score test is developed to check the smooth transition property. The performance of the new proposed model and the score test are examined by some simulations. Applying the log returns of some part of S&P500 and Dow Jones industrial average indexes, we show the competing performance of the ST-HYGARCH model in comparison to HYGARCH and ST-GARCH models in forecasting volatility and value-at-risk.

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47

Thelwell, Richard C., and Ian W. Maynard. "Anxiety-Performance Relationships in Cricketers: Testing the Zone of Optimal Functioning Hypothesis." Perceptual and Motor Skills 87, no. 2 (October 1998): 675–89. http://dx.doi.org/10.2466/pms.1998.87.2.675.

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This study extended Hanin's 1980 Zone of Optimal Functioning hypothesis by assessing both intensity and direction components of competitive state anxiety. 20 volunteer semiprofessional cricketers responded to the modified Competitive State Anxiety Inventory-2 prior to ten matches. For each game, players' performances were intraindividually evaluated by three qualified cricket coaches using subjective criteria. Separate Cognitive Anxiety and Somatic Anxiety ‘below,’ ‘in,’ and ‘above’ zones for Intensity and Direction of state anxiety were identified via repeated empirical assessments. Two two-way analyses of variance were computed for Intensity and Direction of anxiety (Cognitive Anxiety Zone Level x Somatic Anxiety Zone Level) with standardized performance scores as the dependent variable. A significant interaction was found between the zone levels for Cognitive and Somatic Anxiety Direction and performance scores. Examination of the Zone of Optimal Functioning hypothesis that focuses on the interactions between Cognitive and Somatic Anxiety and in particular the directional elements of the modified inventory seems warranted.

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48

Shellie, Krista C., and George L. Hosfield. "Genotype × Environmental Effects on Food Quality of Common Bean: Resource-efficient Testing Procedures." Journal of the American Society for Horticultural Science 116, no. 4 (July 1991): 732–36. http://dx.doi.org/10.21273/jashs.116.4.732.

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Genetic and environmental interactions for bean cooking time, water absorption, and protein content were estimated with 10 dry bean (Phaseolus vulgaris L.) cultivars grown at three locations in Rwanda, Africa, during five consecutive harvests. The genotypic variance component was larger than genotype × environment variance components for the cooking time index and percent water absorption. No significant genotypic effect was observed for seed protein content. The phenotypic correlation (-0.37) between the cooking time index and percent water absorption was not strong enough to justify the use of water absorption as an indirect selection method for cooking time. The most efficient allocation of resources to evaluate the cooking time of common bean cultivars with a 25-pin bar-drop cooker was four field replications over two harvests at two locations. Water absorption was evaluated most efficiently with four field replications over two harvests at a single location.

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49

Schoeman, S. J., and G. G. Jordaan. "Animal x testing environment interaction on postweaning liveweight gains of young bulls." Australian Journal of Agricultural Research 49, no. 4 (1998): 607. http://dx.doi.org/10.1071/a97102.

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Postweaning liveweight gain records of 1610 young bulls obtained both in feedlot and under pasture were used to estimate (co)variance components using a multivariate restricted maximum likelihood analysis. The pedigree file included 3477 animals. Heritability estimates for liveweights and gain in both environments correspond to most previously reported estimates. The genetic correlation of gain between the 2 environments was -0·12, suggesting a large genotype testing environment interaction and re-ranking of animal breeding values across environments. Results of this analysis suggest the need for environment-specific breeding values for postweaning gain.

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50

Naiju, C. D., P. K. Manoj, Tarun Thomas George, and Joseph Kurian. "Study on the Effect of Process Parameters on Reciprocating Wear Behavior of Components Produced by Selective Laser Sintering (SLS)." Advanced Materials Research 488-489 (March 2012): 1424–28. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1424.

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This work presents the results of a study carried out to determine the reciprocating wear behavior and its effect on the process parameters of components manufactured by selective laser sintering (SLS). A standard procedure and specimen had been used in the present study to find the wear behavior. Using Taguchi’s experimental technique, an orthogonal array of modified L9 had been developed. Reciprocating wear testing was carried out and analysis of variance (ANOVA) technique was used to investigate the effect of process parameters and to identify the main process parameter that influences the properties of wear behavior on the SLS components. It has been found that laser power had more influence on wear as compared to other selected process parameters. Micro-structural analysis was also carried out using scanning electron microscope on the wear testing sample.

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