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Journal articles on the topic 'Tests of hypothesis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Campbell, Malcolm. "Hypothesis tests." African Journal of Midwifery and Women's Health 10, no. 4 (October 2, 2016): 154–61. http://dx.doi.org/10.12968/ajmw.2016.10.4.154.

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2

Walker, J. "Hypothesis tests." BJA Education 19, no. 7 (July 2019): 227–31. http://dx.doi.org/10.1016/j.bjae.2019.03.006.

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3

Bulut, Hasan. "AN R PACKAGE FOR MULTIVARIATE HYPOTHESIS TESTS: MVTESTS." E-journal of New World Sciences Academy 14, no. 4 (November 1, 2019): 132–38. http://dx.doi.org/10.12739/nwsa.2019.14.4.2a0175.

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4

Ross, Frederick J. "Hypothesis Tests and p-values." Journal of Psychiatric Practice 17, no. 4 (July 2011): 288–91. http://dx.doi.org/10.1097/01.pra.0000400267.83407.b6.

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5

Wonnacott, Thomas. "Confidence intervals or hypothesis tests?" Journal of Applied Statistics 14, no. 3 (January 1987): 195–201. http://dx.doi.org/10.1080/02664768700000025.

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6

Abelson, Robert P., and Deborah A. Prentice. "Contrast tests of interaction hypothesis." Psychological Methods 2, no. 4 (1997): 315–28. http://dx.doi.org/10.1037/1082-989x.2.4.315.

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7

Guo, M., and D. F. Heitjan. "Multiplicity-calibrated Bayesian hypothesis tests." Biostatistics 11, no. 3 (March 8, 2010): 473–83. http://dx.doi.org/10.1093/biostatistics/kxq012.

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8

Kroll, Charles N., Kelly E. Croteau, and Richard M. Vogel. "Hypothesis tests for hydrologic alteration." Journal of Hydrology 530 (November 2015): 117–26. http://dx.doi.org/10.1016/j.jhydrol.2015.09.057.

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9

Feng, Yanming. "Regression and Hypothesis Tests for Multivariate GNSS State Time Series." Journal of Global Positioning Systems 11, no. 1 (June 30, 2012): 33–45. http://dx.doi.org/10.5081/jgps.11.1.33.

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10

Hewson, Paul. "Introducing the logic of hypothesis tests though randomisation tests." MSOR Connections 14, no. 2 (February 11, 2016): 16. http://dx.doi.org/10.21100/msor.v14i2.259.

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There has been a lot of interest in the use of randomisation tests as a pedagogic alternative to hypothesis tests (Zieffler, 2012), although proposals to use randomisation tests in research are far from new (e.g. Hooton, 1991) with Good (2000) being an updated classic text in this area. This article will present a classroom activity that demonstrates the randomisation tests as a means of understanding several of the concepts around hypothesis testing a manner that is as friendly as possible for maths-phobic and indeed computer-phobic students.

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11

Dey, Rajarshi. "Hypothesis tests with precedence probabilities and precedence-type tests." Wiley Interdisciplinary Reviews: Computational Statistics 10, no. 1 (September 28, 2017): e1417. http://dx.doi.org/10.1002/wics.1417.

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12

Heiser, David A. "Statistical Tests, Tests of Significance, and Tests of a Hypothesis Using Excel." Journal of Modern Applied Statistical Methods 5, no. 2 (November 1, 2005): 551–66. http://dx.doi.org/10.22237/jmasm/1162355280.

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13

McCarthy, Joseph, Mohammad Najand, and Bruce Seifert. "Empirical Tests of the Proxy Hypothesis." Financial Review 25, no. 2 (May 1990): 251–63. http://dx.doi.org/10.1111/j.1540-6288.1990.tb00795.x.

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14

Wilks, D. S. "Resampling Hypothesis Tests for Autocorrelated Fields." Journal of Climate 10, no. 1 (January 1997): 65–82. http://dx.doi.org/10.1175/1520-0442(1997)010<0065:rhtfaf>2.0.co;2.

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15

Ihler, A. T., J. W. Fisher, and A. S. Willsky. "Nonparametric Hypothesis Tests for Statistical Dependency." IEEE Transactions on Signal Processing 52, no. 8 (August 2004): 2234–49. http://dx.doi.org/10.1109/tsp.2004.830994.

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16

ROBACK, PAUL J., and ROBERT A. ASKINS. "Judicious Use of Multiple Hypothesis Tests." Conservation Biology 19, no. 1 (February 2005): 261–67. http://dx.doi.org/10.1111/j.1523-1739.2005.00269.x.

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17

Yang, Shengping. "Hypothesis Tests for a Population Mean." Southwest Respiratory and Critical Care Chronicles 2, no. 5 (January 13, 2014): 52. http://dx.doi.org/10.12746/swrccc.v2i5.117.

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18

Peters, Baron. "Reaction Coordinates and Mechanistic Hypothesis Tests." Annual Review of Physical Chemistry 67, no. 1 (May 27, 2016): 669–90. http://dx.doi.org/10.1146/annurev-physchem-040215-112215.

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19

Caudill, Samuel P., and Robert H. Hill. "Multiple hypothesis tests in multiple investigations." Statistics in Medicine 14, no. 5-7 (March 15, 1995): 585–89. http://dx.doi.org/10.1002/sim.4780140516.

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20

Jones, Michael P., Alissa Beath, Christopher Oldmeadow, and John R. Attia. "Understanding statistical hypothesis tests and power." Medical Journal of Australia 207, no. 4 (August 2017): 148–50. http://dx.doi.org/10.5694/mja16.01022.

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21

Fletcher, Liz. "Vaccine tests key Alzheimer's disease hypothesis." Nature Biotechnology 19, no. 2 (February 2001): 104–5. http://dx.doi.org/10.1038/84340.

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22

Novikov, Andrey, and Juan Luis Palacios-Soto. "Sequential hypothesis tests under random horizon." Sequential Analysis 39, no. 2 (April 2, 2020): 133–66. http://dx.doi.org/10.1080/07474946.2020.1766875.

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23

Bernard, Andrew B., and Steven N. Durlauf. "Interpreting tests of the convergence hypothesis." Journal of Econometrics 71, no. 1-2 (March 1996): 161–73. http://dx.doi.org/10.1016/0304-4076(94)01699-2.

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24

Anderson, Rodney P., and John R. Menninger. "Tests of the ribosome editor hypothesis." Molecular and General Genetics MGG 209, no. 2 (September 1987): 313–18. http://dx.doi.org/10.1007/bf00329659.

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25

Cohen, Samuel N., Timo Henckel, Gordon D. Menzies, Johannes Muhle-Karbe, and Daniel J. Zizzo. "Switching cost models as hypothesis tests." Economics Letters 175 (February 2019): 32–35. http://dx.doi.org/10.1016/j.econlet.2018.11.014.

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26

Banker, Rajiv D. "Hypothesis tests using data envelopment analysis." Journal of Productivity Analysis 7, no. 2-3 (July 1996): 139–59. http://dx.doi.org/10.1007/bf00157038.

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27

Häfner, H. "Further tests on the estrogen hypothesis." Schizophrenia Research 18, no. 2-3 (February 1996): 111. http://dx.doi.org/10.1016/0920-9964(96)85384-9.

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28

Dember, William N., Richard S. Melton, Dao Q. Nguyen, and Steven R. Howe. "Meta-emotion: Tests of the Lutz hypothesis." Bulletin of the Psychonomic Society 31, no. 6 (June 1993): 579–82. http://dx.doi.org/10.3758/bf03337360.

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29

Gissane, C. "The problem of too many hypothesis tests." Physiotherapy Practice and Research 38, no. 1 (December 30, 2016): 67–68. http://dx.doi.org/10.3233/ppr-160088.

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30

Alqatawneh, Alaa, Rania Alhalaseh, Ahmad Hassanat, and Mohammad Abbadi. "Statistical-Hypothesis-Aided Tests for Epilepsy Classification." Computers 8, no. 4 (November 20, 2019): 84. http://dx.doi.org/10.3390/computers8040084.

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In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results ( 98.4 % ) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results ( 94.0 % ) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results.

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31

Leland, Jonathan W., and Jordan Grafman. "Experimental tests of the Somatic Marker hypothesis." Games and Economic Behavior 52, no. 2 (August 2005): 386–409. http://dx.doi.org/10.1016/j.geb.2004.09.001.

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32

Hamill, Thomas M. "Hypothesis Tests for Evaluating Numerical Precipitation Forecasts." Weather and Forecasting 14, no. 2 (April 1999): 155–67. http://dx.doi.org/10.1175/1520-0434(1999)014<0155:htfenp>2.0.co;2.

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33

Reschenhofer, Erhard. "Robust tests of the random walk hypothesis." Quantitative Finance 4, no. 6 (December 1, 2004): 57–60. http://dx.doi.org/10.1080/14697680500040322.

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34

McFatter, Robert M., and Harry F. Gollob. "The Power of Hypothesis Tests for Comparisons." Educational and Psychological Measurement 46, no. 4 (December 1986): 883–86. http://dx.doi.org/10.1177/001316448604600408.

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35

Horowitz, Joel L. "The bootstrap and hypothesis tests in econometrics." Journal of Econometrics 100, no. 1 (January 2001): 37–40. http://dx.doi.org/10.1016/s0304-4076(00)00051-8.

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36

Efron, B. "The length heuristic for simultaneous hypothesis tests." Biometrika 84, no. 1 (March 1, 1997): 143–57. http://dx.doi.org/10.1093/biomet/84.1.143.

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37

KENNEDY, PETER. "NON-NESTED HYPOTHESIS TESTS: A DIAGRAMMATIC EXPOSITION." Australian Economic Papers 28, no. 52 (June 1989): 160–65. http://dx.doi.org/10.1111/j.1467-8454.1989.tb00466.x.

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38

Sterne, Jonathan A. C. "Teaching hypothesis tests - time for significant change?" Statistics in Medicine 21, no. 7 (2002): 985–94. http://dx.doi.org/10.1002/sim.1129.

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39

Killeen, P. R. "An Alternative to Null-Hypothesis Significance Tests." Psychological Science 16, no. 5 (May 1, 2005): 345–53. http://dx.doi.org/10.1111/j.0956-7976.2005.01538.x.

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40

KENT, JOHN T. "The underlying structure of nonnested hypothesis tests." Biometrika 73, no. 2 (1986): 333–43. http://dx.doi.org/10.1093/biomet/73.2.333.

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41

Merz, Rachel A., and Sarah A. Woodin. "Hooked setae: tests of the anchor hypothesis." Invertebrate Biology 119, no. 1 (May 11, 2005): 67–82. http://dx.doi.org/10.1111/j.1744-7410.2000.tb00175.x.

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42

Kim, Sangjin, and Paul Schliekelman. "Prioritizing hypothesis tests for high throughput data." Bioinformatics 32, no. 6 (November 16, 2015): 850–58. http://dx.doi.org/10.1093/bioinformatics/btv608.

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Abstract Motivation: The advent of high throughput data has led to a massive increase in the number of hypothesis tests conducted in many types of biological studies and a concomitant increase in stringency of significance thresholds. Filtering methods, which use independent information to eliminate less promising tests and thus reduce multiple testing, have been widely and successfully applied. However, key questions remain about how to best apply them: When is filtering beneficial and when is it detrimental? How good does the independent information need to be in order for filtering to be effective? How should one choose the filter cutoff that separates tests that pass the filter from those that don’t? Result: We quantify the effect of the quality of the filter information, the filter cutoff and other factors on the effectiveness of the filter and show a number of results: If the filter has a high probability (e.g. 70%) of ranking true positive features highly (e.g. top 10%), then filtering can lead to dramatic increase (e.g. 10-fold) in discovery probability when there is high redundancy in information between hypothesis tests. Filtering is less effective when there is low redundancy between hypothesis tests and its benefit decreases rapidly as the quality of the filter information decreases. Furthermore, the outcome is highly dependent on the choice of filter cutoff. Choosing the cutoff without reference to the data will often lead to a large loss in discovery probability. However, naïve optimization of the cutoff using the data will lead to inflated type I error. We introduce a data-based method for choosing the cutoff that maintains control of the family-wise error rate via a correction factor to the significance threshold. Application of this approach offers as much as a several-fold advantage in discovery probability relative to no filtering, while maintaining type I error control. We also introduce a closely related method of P-value weighting that further improves performance. Availability and implementation: R code for calculating the correction factor is available at http://www.stat.uga.edu/people/faculty/paul-schliekelman. Contact: pdschlie@stat.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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43

Turner, Rolf, and Celeste Jeffs. "An extension of Monte Carlo hypothesis tests." Communications in Statistics - Simulation and Computation 46, no. 8 (September 3, 2016): 6545–58. http://dx.doi.org/10.1080/03610918.2016.1208232.

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44

Sedgwick, P. "One sided and two sided hypothesis tests." BMJ 340, may12 1 (May 12, 2010): c2458. http://dx.doi.org/10.1136/bmj.c2458.

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45

Zimmerman, Donald W. "Heterogeneity of variance and biased hypothesis tests." Journal of Applied Statistics 40, no. 1 (November 16, 2012): 169–93. http://dx.doi.org/10.1080/02664763.2012.740620.

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46

Baringhaus, Ludwig. "Asymptotic optimality of multivariate linear hypothesis tests." Journal of Multivariate Analysis 23, no. 2 (December 1987): 303–11. http://dx.doi.org/10.1016/0047-259x(87)90159-x.

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47

Fraiman, Ricardo, Leonardo Moreno, and Sebastian Vallejo. "Some hypothesis tests based on random projection." Computational Statistics 32, no. 3 (May 13, 2017): 1165–89. http://dx.doi.org/10.1007/s00180-017-0732-4.

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48

Abul Naga, Ramses H., Yajie Shen, and Hong Il Yoo. "Joint hypothesis tests for multidimensional inequality indices." Economics Letters 141 (April 2016): 138–42. http://dx.doi.org/10.1016/j.econlet.2016.02.010.

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49

Cho, Jin Seo, and Halbert White. "Generalized runs tests for the IID hypothesis." Journal of Econometrics 162, no. 2 (June 2011): 326–44. http://dx.doi.org/10.1016/j.jeconom.2011.02.001.

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50

Handa, Jagdish, and Barry K. Ma. "Four tests for the random walk hypothesis." Economics Letters 29, no. 2 (January 1989): 141–45. http://dx.doi.org/10.1016/0165-1765(89)90264-4.

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