Academic literature on the topic 'Test of homogeneity'
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Journal articles on the topic "Test of homogeneity"
Li, Billy Y. G., and George E. Booney. "Test of Homogeneity." Biometrics 52, no. 4 (December 1996): 1521. http://dx.doi.org/10.2307/2532867.
Full textWaldman, David A., and Bruce J. Avolio. "Homogeneity of test validity." Journal of Applied Psychology 74, no. 2 (April 1989): 371–74. http://dx.doi.org/10.1037/0021-9010.74.2.371.
Full textBrkich, D. M. "B-test of homogeneity." Microelectronics Reliability 27, no. 4 (January 1987): 639–41. http://dx.doi.org/10.1016/0026-2714(87)90008-4.
Full textUshakov, V. G., and N. G. Ushakov. "On one test of homogeneity." Doklady Mathematics 93, no. 3 (May 2016): 310–12. http://dx.doi.org/10.1134/s106456241603025x.
Full textFlores, Ramón, Rosa Lillo, and Juan Romo. "Homogeneity test for functional data." Journal of Applied Statistics 45, no. 5 (May 4, 2017): 868–83. http://dx.doi.org/10.1080/02664763.2017.1319470.
Full textFujii, Yoshinori. "ON HOMOGENEITY TEST USING ESTIMATING FUNCTION." Bulletin of informatics and cybernetics 26, no. 1/2 (March 1994): 101–7. http://dx.doi.org/10.5109/13436.
Full textFearn, Tom, and Michael Thompson. "A new test for ‘sufficient homogeneity’." Analyst 126, no. 8 (2001): 1414–17. http://dx.doi.org/10.1039/b103812p.
Full textThompson, Michael. "Is your ‘homogeneity test’ really useful?" Analytical Methods 7, no. 4 (2015): 1627–29. http://dx.doi.org/10.1039/c4ay02762k.
Full textHayakawa, Takesi. "Test of homogeneity of multiple parameters." Journal of Statistical Planning and Inference 38, no. 3 (March 1994): 351–57. http://dx.doi.org/10.1016/0378-3758(94)90015-9.
Full textMa, Changxing, Guogen Shan, and Song Liu. "Homogeneity Test for Correlated Binary Data." PLOS ONE 10, no. 4 (April 21, 2015): e0124337. http://dx.doi.org/10.1371/journal.pone.0124337.
Full textDissertations / Theses on the topic "Test of homogeneity"
Wu, Baohua. "Data Driven Approaches to Testing Homogeneity of Intraclass Correlation Coefficients." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/math_theses/92.
Full textMu, Zhiqiang. "Comparing the Statistical Tests for Homogeneity of Variances." Digital Commons @ East Tennessee State University, 2006. https://dc.etsu.edu/etd/2212.
Full textNian, Gaowei. "A score test of homogeneity in generalized additive models for zero-inflated count data." Kansas State University, 2014. http://hdl.handle.net/2097/18230.
Full textDepartment of Statistics
Wei-Wen Hsu
Zero-Inflated Poisson (ZIP) models are often used to analyze the count data with excess zeros. In the ZIP model, the Poisson mean and the mixing weight are often assumed to depend on covariates through regression technique. In other words, the effect of covariates on Poisson mean or the mixing weight is specified using a proper link function coupled with a linear predictor which is simply a linear combination of unknown regression coefficients and covariates. However, in practice, this predictor may not be linear in regression parameters but curvilinear or nonlinear. Under such situation, a more general and flexible approach should be considered. One popular method in the literature is Zero-Inflated Generalized Additive Models (ZIGAM) which extends the zero-inflated models to incorporate the use of Generalized Additive Models (GAM). These models can accommodate the nonlinear predictor in the link function. For ZIGAM, it is also of interest to conduct inferences for the mixing weight, particularly evaluating whether the mixing weight equals to zero. Many methodologies have been proposed to examine this question, but all of them are developed under classical zero-inflated models rather than ZIGAM. In this report, we propose a generalized score test to evaluate whether the mixing weight is equal to zero under the framework of ZIGAM with Poisson model. Technically, the proposed score test is developed based on a novel transformation for the mixing weight coupled with proportional constraints on ZIGAM, where it assumes that the smooth components of covariates in both the Poisson mean and the mixing weight have proportional relationships. An intensive simulation study indicates that the proposed score test outperforms the other existing tests when the mixing weight and the Poisson mean truly involve a nonlinear predictor. The recreational fisheries data from the Marine Recreational Information Program (MRIP) survey conducted by National Oceanic and Atmospheric Administration (NOAA) are used to illustrate the proposed methodology.
Stewart, Michael Ian. "Asymptotic methods for tests of homogeneity for finite mixture models." Thesis, The University of Sydney, 2002. http://hdl.handle.net/2123/855.
Full textStewart, Michael Ian. "Asymptotic methods for tests of homogeneity for finite mixture models." University of Sydney. Mathematics and Statistics, 2002. http://hdl.handle.net/2123/855.
Full textHöge, Elisabet. "Test and Analysis of Homogeneity Regarding Failure Intensity of Components in Nuclear Power Plants." Thesis, Uppsala universitet, Matematiska institutionen, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-162564.
Full textOsaka, Haruki. "Asymptotics of Mixture Model Selection." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27230.
Full textBagdonavičius, Vilijandas B., Ruta Levuliene, Mikhail S. Nikulin, and Olga Zdorova-Cheminade. "Tests for homogeneity of survival distributions against non-location alternatives and analysis of the gastric cancer data." Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2011/5152/.
Full textGao, Siyu. "The impact of misspecification of nuisance parameters on test for homogeneity in zero-inflated Poisson model: a simulation study." Kansas State University, 2014. http://hdl.handle.net/2097/17804.
Full textDepartment of Statistics
Wei-Wen Hsu
The zero-inflated Poisson (ZIP) model consists of a Poisson model and a degenerate distribution at zero. Under this model, zero counts are generated from two sources, representing a heterogeneity in the population. In practice, it is often interested to evaluate this heterogeneity is consistent with the observed data or not. Most of the existing methodologies to examine this heterogeneity are often assuming that the Poisson mean is a function of nuisance parameters which are simply the coefficients associated with covariates. However, these nuisance parameters can be misspecified when performing these methodologies. As a result, the validity and the power of the test may be affected. Such impact of misspecification has not been discussed in the literature. This report primarily focuses on investigating the impact of misspecification on the performance of score test for homogeneity in ZIP models. Through an intensive simulation study, we find that: 1) under misspecification, the limiting distribution of the score test statistic under the null no longer follows a chi-squared distribution. A parametric bootstrap methodology is suggested to use to find the true null limiting distribution of the score test statistic; 2) the power of the test decreases as the number of covariates in the Poisson mean increases. The test with a constant Poisson mean has the highest power, even compared to the test with a well-specified mean. At last, simulation results are applied to the Wuhan Inpatient Care Insurance data which contain excess zeros.
Carvalho, Helton Graziadei de. "Testes bayesianos para homogeneidade marginal em tabelas de contingência." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-27082015-181850/.
Full textTests of hypotheses for marginal proportions in contingency tables play a fundamental role, for instance, in the investigation of behaviour (or opinion) change. However, most texts in the literature are concerned with tests that assume independent populations (e.g: homogeneity tests). There are some works that explore hypotheses tests for dependent proportions such as the McNemar Test for 2 x 2 contingency tables. The generalization of McNemar test for k x k contingency tables, called marginal homogeneity test, usually requires asymptotic approximations. Nevertheless, for small sample sizes or sparse tables, such methods may occasionally produce imprecise results. In this work, we review some classical and Bayesian measures of evidence commonly applied to compare two marginal proportions. We propose the Full Bayesian Significance Test (FBST) to investigate marginal homogeneity in two-way and multidimensional contingency tables. The FBST is based on a measure of evidence, called e-value, which does not depend on asymptotic results, does not violate the likelihood principle and satisfies logical properties that are expected from hypothesis testing. Consequently, the FBST approach to test marginal homogeneity overcomes several limitations usually met by other procedures.
Books on the topic "Test of homogeneity"
Caramani, Danièle. The measurement of territorial homogeneity: A test on comparative electoral data since 1832. Badia Fiesolana, San Domenico (FI): European University Institute, 2002.
Find full textBöhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.
Find full textStewart, Connie, and Rose McCloskey. Learn to Use the Chi-Square Homogeneity Test in Minitab With Data From a 2015 Health Care Observational Study. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488237.
Full textScott Jones, Julie. Learn to Use Bartlett’s Test of Homogeneity of Variances in SPSS With Data From the General Social Survey (2016–17). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526474698.
Full textScott Jones, Julie. Learn to Use Bartlett’s Test of Homogeneity of Variances in R With Data From the General Social Survey (2016–17). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526496652.
Full textScott Jones, Julie. Learn to Use Bartlett’s Test of Homogeneity of Variances in Stata With Data From the General Social Survey (2016–17). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498649.
Full textMadden, David. Omitted variables, dynamic specification and tests for homogeneity. Dublin: University College Dublin, Department of Economics, 1994.
Find full textThe Greek text of Ezekiel: An examination of its homogeneity. Atlanta, Ga: Scholars Press, 1985.
Find full textKössler, W. Some c-sample rank tests of homogeneity against ordered alternatives based on U-statistics. Berlin: Professoren des Institutes für Informatik, Humboldt-Universität zu Berlin, 2004.
Find full textKössler, W. Some c-sample rank tests of homogeneity against ordered alternatives based on U-statistics. Berlin: Professoren des Institutes für Informatik, Humboldt-Universität zu Berlin, 2004.
Find full textBook chapters on the topic "Test of homogeneity"
Skousen, Royal. "A Natural Test for Homogeneity." In Analogy and Structure, 246–65. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-8098-4_12.
Full textGooch, Jan W. "Chi-square Test of Homogeneity." In Encyclopedic Dictionary of Polymers, 973. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15177.
Full textWingate, LaRicka R. "Black Intelligence Test of Cultural Homogeneity." In Encyclopedia of Child Behavior and Development, 261–62. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_367.
Full textWest, Jennifer M. "Black Intelligence Test of Cultural Homogeneity (B.I.T.C.H.)." In Encyclopedia of Cross-Cultural School Psychology, 164–65. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-71799-9_46.
Full textMantel, Klaus, and Johannes Schwider. "Interferometric Homogeneity Test Using Adaptive Frequency Comb Illumination." In Fringe 2013, 393–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36359-7_71.
Full textGrzegorzewski, Przemysław, and Hubert Szymanowski. "Chi-Square Test for Homogeneity with Fuzzy Data." In Strengthening Links Between Data Analysis and Soft Computing, 151–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10765-3_18.
Full textBarone-Adesi, Giovanni, Patrick Gagliardini, and Giovanni Urga. "A Test of the Homogeneity of Asset pricing Models." In Multi-moment Asset Allocation and Pricing Models, 223–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119201830.ch9.
Full textWiltshire, Stephen, and Max Beran. "A Significance Test for Homogeneity of Flood Frequency Regions." In Regional Flood Frequency Analysis, 147–58. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3959-2_12.
Full textZhang, Fan, Jing Zhao, Chao Zhao, Gang Wu, Xinyu Cao, and Haitao Wang. "Discussion on the Homogeneity Test for Microbiological Reference Materials." In Advances in Manufacturing, Production Management and Process Control, 348–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51981-0_44.
Full textChow, Shein-Chung, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina. "Test for Homogeneity of Two Zero-Inflated Poisson Population." In Sample Size Calculations in Clinical Research: Third Edition, 349–72. Third edition. | Boca Raton : Taylor & Francis, 2017. | Series: Chapman & Hall/CRC biostatistics series | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315183084-16.
Full textConference papers on the topic "Test of homogeneity"
Biao Chen, P. Willett, and R. Streit. "Transient detection using a homogeneity test." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.761318.
Full textRos, Faizah Che, Hiroyuki Tosaka, Kenji Sasaki, Lariyah Mohd Sidek, and Hidayah Basri. "Absolute homogeneity test of Kelantan catchment precipitation series." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICoMEIA 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4915661.
Full textWang, Yawei, Xueli Wang, and Meng Xiao. "The Homogeneity and Heterogeneity Hypothesis Test of the Relative Risk." In 2012 International Conference on Business Computing and Global Informatization (BCGIN). IEEE, 2012. http://dx.doi.org/10.1109/bcgin.2012.156.
Full textGarcia-Balboa, Jose L., Maria V. Alba-Fernandez, Francisco J. Ariza-Lopez, and Joso Rodriguez-Avi. "Homogeneity Test for Confusion Matrices: A Method and an Example." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517924.
Full textChen, Yi-Ting. "Introduction to Statistical Methods for Outlier Detection and Sample Homogeneity Assessment of Reference Materials and Proficiency Test Items." In NCSL International Workshop & Symposium. NCSL International, 2021. http://dx.doi.org/10.51843/wsproceedings.2021.15.
Full textBandiera, Francesco, Angelo Coluccia, and Giuseppe Ricci. "A test of homogeneity for RSS measurements within a wireless sensor network." In 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2014. http://dx.doi.org/10.1109/inista.2014.6873594.
Full textTaleb, Y., and E. A. K. Cohen. "A wavelet based likelihood ratio test for the homogeneity of poisson processes." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551768.
Full textPhillips, Rhonda D., Layne T. Watson, and Randolph H. Wynne. "A Fuzzy Homogeneity Test for the Iterative Guided Spectral Class Rejection Algorithm." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779136.
Full textSun, Fengbin. "System Global Acceleration Factor and Life Test Design Considering Stress Non-homogeneity Across Components." In 2019 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2019. http://dx.doi.org/10.1109/rams.2019.8769011.
Full textYang, Wenku, Yujing Yang, Tao Yang, and Wenrong Deng. "Simple and easy automation test and measurement method of large-size optical glass homogeneity." In Photonics China '98, edited by Shenghua Ye. SPIE, 1998. http://dx.doi.org/10.1117/12.318433.
Full textReports on the topic "Test of homogeneity"
Solomon, H., and M. A. Stephens. An Extension of Cochran's Test for Homogeneity of Variances. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada211054.
Full textLevisohn, Sharon, Mark Jackwood, and Stanley Kleven. New Approaches for Detection of Mycoplasma iowae Infection in Turkeys. United States Department of Agriculture, February 1995. http://dx.doi.org/10.32747/1995.7612834.bard.
Full textShoseyov, Oded, Steven A. Weinbaum, Raphael Goren, and Abhaya M. Dandekar. Biological Thinning of Fruit Set by RNAase in Deciduous Fruit Trees. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568110.bard.
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