Journal articles on the topic 'Two sample testing'

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1

Städler, Nicolas, and Sach Mukherjee. "Two-sample testing in high dimensions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 1 (April 9, 2016): 225–46. http://dx.doi.org/10.1111/rssb.12173.

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Holmes, Chris C., François Caron, Jim E. Griffin, and David A. Stephens. "Two-sample Bayesian Nonparametric Hypothesis Testing." Bayesian Analysis 10, no. 2 (June 2015): 297–320. http://dx.doi.org/10.1214/14-ba914.

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3

Tsong, Yi, and Wen-Jen Chen. "Noninferiority Testing Beyond Simple Two-Sample Comparison." Journal of Biopharmaceutical Statistics 17, no. 2 (February 23, 2007): 289–308. http://dx.doi.org/10.1080/10543400601177368.

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4

Ramsey, Philip, and Patricia Ramsey. "Testing Variability in the Two-Sample Case." Communications in Statistics - Simulation and Computation 36, no. 2 (2007): 233–48. http://dx.doi.org/10.1080/03610910601158310.

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5

Gillam, T. P. S., and C. G. Lester. "Biased bootstrap sampling for efficient two-sample testing." Journal of Instrumentation 13, no. 12 (December 10, 2018): P12014. http://dx.doi.org/10.1088/1748-0221/13/12/p12014.

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6

Gönen, Mithat, Peter H. Westfall, and Wesley O. Johnson. "Bayesian Multiple Testing for Two-Sample Multivariate Endpoints." Biometrics 59, no. 1 (March 2003): 76–82. http://dx.doi.org/10.1111/1541-0420.00009.

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7

Liu, Yukun, Pengfei Li, and Yuejiao Fu. "Testing Homogeneity in a Semiparametric Two-Sample Problem." Journal of Probability and Statistics 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/537474.

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We study a two-sample homogeneity testing problem, in which one sample comes from a population with densityf(x)and the other is from a mixture population with mixture density(1−λ)f(x)+λg(x). This problem arises naturally from many statistical applications such as test for partial differential gene expression in microarray study or genetic studies for gene mutation. Under the semiparametric assumptiong(x)=f(x)eα+βx, a penalized empirical likelihood ratio test could be constructed, but its implementation is hindered by the fact that there is neither feasible algorithm for computing the test statistic nor available research results on its theoretical properties. To circumvent these difficulties, we propose an EM test based on the penalized empirical likelihood. We prove that the EM test has a simple chi-square limiting distribution, and we also demonstrate its competitive testing performances by simulations. A real-data example is used to illustrate the proposed methodology.
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8

Ghoshdastidar, Debarghya, Maurilio Gutzeit, Alexandra Carpentier, and Ulrike von Luxburg. "Two-sample hypothesis testing for inhomogeneous random graphs." Annals of Statistics 48, no. 4 (August 2020): 2208–29. http://dx.doi.org/10.1214/19-aos1884.

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9

Feng, Shixia, Qiwei Liang, Robin D. Kinser, Kirk Newland, and Rudolf Guilbaud. "Testing equivalence between two laboratories or two methods using paired-sample analysis and interval hypothesis testing." Analytical and Bioanalytical Chemistry 385, no. 5 (June 7, 2006): 975–81. http://dx.doi.org/10.1007/s00216-006-0417-2.

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10

Zhu, Shengyu, Biao Chen, Zhitang Chen, and Pengfei Yang. "Asymptotically Optimal One- and Two-Sample Testing With Kernels." IEEE Transactions on Information Theory 67, no. 4 (April 2021): 2074–92. http://dx.doi.org/10.1109/tit.2021.3059267.

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11

Eder, Yoni, and Koby Todros. "Robust Two-Sample Location Testing via Probability Measure Transform." IEEE Transactions on Signal Processing 69 (2021): 4724–39. http://dx.doi.org/10.1109/tsp.2021.3092380.

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12

Reitmeir, Peter, and Gernot Wassmer. "One-sided multiple endpoint testing in two-sample comparisons." Communications in Statistics - Simulation and Computation 25, no. 1 (January 1996): 99–117. http://dx.doi.org/10.1080/03610919608813301.

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13

Pesarin, Fortunato, Luigi Salmaso, Eleonora Carrozzo, and Rosa Arboretti. "Union–intersection permutation solution for two-sample equivalence testing." Statistics and Computing 26, no. 3 (March 1, 2015): 693–701. http://dx.doi.org/10.1007/s11222-015-9552-y.

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14

Qiu, Shi-Fang, Nian-Sheng Tang, Man-Lai Tang, and Yan-Bo Pei. "Sample Size for Testing Difference Between Two Proportions for the Bilateral-Sample Design." Journal of Biopharmaceutical Statistics 19, no. 5 (August 7, 2009): 857–71. http://dx.doi.org/10.1080/10543400903105372.

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15

Davis, Alan B. "Power of testing proportions in small two-sample studies when sample sizes are equal." Statistics in Medicine 12, no. 8 (April 30, 1993): 777–87. http://dx.doi.org/10.1002/sim.4780120806.

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16

Tang, Minh, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, and Carey E. Priebe. "A nonparametric two-sample hypothesis testing problem for random graphs." Bernoulli 23, no. 3 (August 2017): 1599–630. http://dx.doi.org/10.3150/15-bej789.

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17

Hediger, Simon, Loris Michel, and Jeffrey Näf. "On the use of random forest for two-sample testing." Computational Statistics & Data Analysis 170 (June 2022): 107435. http://dx.doi.org/10.1016/j.csda.2022.107435.

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18

Murakami, Hidetoshi. "A randomized Baumgartner statistic for multivariate two-sample testing hypothesis." Journal of Statistical Computation and Simulation 85, no. 1 (June 25, 2013): 189–201. http://dx.doi.org/10.1080/00949655.2013.809087.

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19

Guo, Jiin-Huarng, Hubert J. Chen, and Wei-Ming Luh. "Optimal Sample Sizes for Testing the Equivalence of Two Means." Methodology 15, no. 3 (August 1, 2019): 128–36. http://dx.doi.org/10.1027/1614-2241/a000171.

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Abstract. Equivalence tests (also known as similarity or parity tests) have become more and more popular in addition to equality tests. However, in testing the equivalence of two population means, approximate sample sizes developed using conventional techniques found in the literature on this topic have usually been under-valued as having less statistical power than is required. In this paper, the authors first address the reason for this problem and then provide a solution using an exhaustive local search algorithm to find the optimal sample size. The proposed method is not only accurate but is also flexible so that unequal variances or sampling unit costs for different groups can be considered using different sample size allocations. Figures and a numerical example are presented to demonstrate various configurations. An R Shiny App is also available for easy use ( https://optimal-sample-size.shinyapps.io/equivalence-of-means/ ).
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20

Wilcox, Rand R. "Two-Sample, Bivariate Hypothesis Testing Methods Based on Tukey's Depth." Multivariate Behavioral Research 38, no. 2 (April 2003): 225–46. http://dx.doi.org/10.1207/s15327906mbr3802_4.

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21

Marozzi, Marco, and Luigi Salmaso. "Multivariate Bi-Aspect Testing for the Two-Sample Location Problem." Communications in Statistics - Theory and Methods 35, no. 3 (April 2006): 477–88. http://dx.doi.org/10.1080/03610920500476515.

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22

Wang, Min, and Guangying Liu. "A Simple Two-Sample Bayesian t-Test for Hypothesis Testing." American Statistician 70, no. 2 (April 2, 2016): 195–201. http://dx.doi.org/10.1080/00031305.2015.1093027.

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23

Montesinos-López, Osval Antonio, Kent Eskridge, Abelardo Montesinos-López, and José Crossa. "Optimal sample sizes for group testing in two-stage sampling." Seed Science Research 25, no. 01 (November 28, 2014): 12–28. http://dx.doi.org/10.1017/s096025851400035x.

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24

Tang, Minh, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, Youngser Park, and Carey E. Priebe. "A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs." Journal of Computational and Graphical Statistics 26, no. 2 (April 3, 2017): 344–54. http://dx.doi.org/10.1080/10618600.2016.1193505.

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25

Zhao, Jing, Sanying Feng, and Yuping Hu. "Two-Sample Hypothesis Test for Functional Data." Mathematics 10, no. 21 (November 1, 2022): 4060. http://dx.doi.org/10.3390/math10214060.

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In this paper, we develop and study a novel testing procedure that has more a powerful ability to detect mean difference for functional data. In general, it includes two stages: first, splitting the sample into two parts and selecting principle components adaptively based on the first half-sample; then, constructing a test statistic based on another half-sample. An extensive simulation study is presented, which shows that the proposed test works very well in comparison with several other methods in a variety of alternative settings.
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26

Bonett, Douglas G. "Sample Size Requirements for Comparing Two Alpha Coefficients." Applied Psychological Measurement 27, no. 1 (January 2003): 72–74. http://dx.doi.org/10.1177/0146621602239477.

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Graphs and tables are currently available for approximating the sample size needed to test the equality of two alpha reliability coefficients with desired power. These tables and graphs are limited to particular values of Type I error, power, and effect size. General formulas are derived to determine the sample size requirements for hypothesis testing with desired power and interval estimation with desired precision.
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27

Narayan, K. M. Venkat, and Ram Jagannathan. "Two in One: Diagnosing Type 2 Diabetes With Single-Sample Testing." Annals of Internal Medicine 169, no. 3 (June 19, 2018): 193. http://dx.doi.org/10.7326/m18-1477.

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28

Dabrowska, Dorota M., and Kjell A. Doksum. "Estimation and Testing in a Two-Sample Generalized Odds-Rate Model." Journal of the American Statistical Association 83, no. 403 (September 1988): 744–49. http://dx.doi.org/10.1080/01621459.1988.10478657.

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29

Arboretti Giancristofaro, Rosa, Stefano Bonnini, and Fortunato Pesarin. "A permutation approach for testing heterogeneity in two-sample categorical variables." Statistics and Computing 19, no. 2 (August 1, 2008): 209–16. http://dx.doi.org/10.1007/s11222-008-9085-8.

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30

Zhou, Yan-qiu, Yan-ling Wan, and Tao Zhang. "Two-sample Testing for Mean Functions with Incompletely Observed Functional Data." Acta Mathematicae Applicatae Sinica, English Series 36, no. 2 (March 2020): 374–89. http://dx.doi.org/10.1007/s10255-020-0934-3.

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31

Ramdas, Aaditya, Nicolás Trillos, and Marco Cuturi. "On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests." Entropy 19, no. 2 (January 26, 2017): 47. http://dx.doi.org/10.3390/e19020047.

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32

Subramanian, Sundarraman. "Function-based hypothesis testing in censored two-sample location-scale models." Lifetime Data Analysis 26, no. 1 (December 11, 2018): 183–213. http://dx.doi.org/10.1007/s10985-018-09456-8.

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33

Lui, Kung-Jong, Xiao-Hua Zhou, and Chii-Dean Lin. "Testing Equality between Two Diagnostic Procedures in Paired-Sample Ordinal Data." Biometrical Journal 46, no. 6 (December 2004): 642–52. http://dx.doi.org/10.1002/bimj.200410062.

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34

Wang, Rui, Stephen W. Lagakos, and Robert J. Gray. "Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring." Biostatistics 11, no. 4 (May 2, 2010): 676–92. http://dx.doi.org/10.1093/biostatistics/kxq021.

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35

Meyer, Denny H. "Testing for significant changes in popularity." South African Journal of Business Management 19, no. 3 (September 30, 1988): 96–98. http://dx.doi.org/10.4102/sajbm.v19i3.978.

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A graphic procedure for determining whether a statistically significant change in television ratings has occurred is proposed. The method takes into account the fact that the ratings are generated from the same sample of viewers rather than from two independent samples. In addition it incorporates the effect of sample size. The method is applicable for the analysis of all market research data collected by such panel methods.
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36

Bhatt, N., J. Chitranshi, and M. Mehta. "Testing Herzberg’s two factor theory on millennials." CARDIOMETRY, no. 22 (May 25, 2022): 231–36. http://dx.doi.org/10.18137/cardiometry.2022.22.231236.

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This study tests Herzberg’s two-factor theory of motivation on working millennials. The purpose of the study is to evaluate what the assumptions and the findings of the original study hold for millennials. With the increasing trend of amalgamation of work life and personal life, job satisfaction and general job happiness have never been more on an individual’s personal life. This impact is far-reaching, with strong indications of affecting the employees’ physical, mental, and social health. In such a scenario, organizations need to keep their employees motivated and satisfied. For studying Herzberg’s two-factor theory of motivation on working millennials, we have selected respondents with work experience of at least one year. We have not restricted the study to any particular industry; hence our sample is across all industries. The type of sampling used was convenient sampling. From the analysis, we have found that for the concerned sample, hygiene factors tower above the motivating factors in terms of job satisfaction for millennials. From the data collection and analysis, we proved that both motivators and hygiene factors affect job satisfaction, unlike what has been said by Herzberg. This study will contribute to the exciting literature on motivation.
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37

Nikam, Chaitali, Caesar Sengupta, Kallathikumar K, Krishnakumar S, and PraveenKumar Ganesan. "1 to 1, 11,111 Covid sample testing; Decoding the testing matrix!" Asian Journal of Medical Sciences 12, no. 3 (March 1, 2021): 1–9. http://dx.doi.org/10.3126/ajms.v12i3.32481.

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Almost 100 yrs. after the 1918 Spanish Flu Pandemic, world is witnessing “Covid-19”, another Pandemic of similar scale. Interesting, parallels are being drawn between these two pandemics –occurring a century apart; on their scale of spread, potential impact, global scare and attention, containment measures and even people are framing similar projections on possible course of the pandemic. While world was equally unprepared on many terms to safeguard itself from such a pandemic from SARS Cov-2 Virus, one scenario quite distinct from the previous pandemic scenario, is the current status of laboratories with advanced tools. Ground breaking genomic technologies such as NAAT (Nucleic acid Amplification technologies) or PCR (Polymerase Chain Reaction), which currently exist can be positioned as crucial weapons strategically for both community level operations and patient level care. We have the opportunity for cross learning from the experiences of the laboratories involved in Covid testing while informing our peers across policy, diagnostic and research domains, exciting the newcomers to join forces and educating all our associates who can be involved in supportive roles and the large numbers of innovators / supporters who can offer to improvise and strengthen on our current solutions.
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38

Cheng, Xiuyuan, Alexander Cloninger, and Ronald R. Coifman. "Two-sample statistics based on anisotropic kernels." Information and Inference: A Journal of the IMA 9, no. 3 (December 10, 2019): 677–719. http://dx.doi.org/10.1093/imaiai/iaz018.

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Abstract The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) statistic for measuring the distance between two distributions given finitely many multivariate samples. When the distributions are locally low-dimensional, the proposed test can be made more powerful to distinguish certain alternatives by incorporating local covariance matrices and constructing an anisotropic kernel. The kernel matrix is asymmetric; it computes the affinity between $n$ data points and a set of $n_R$ reference points, where $n_R$ can be drastically smaller than $n$. While the proposed statistic can be viewed as a special class of Reproducing Kernel Hilbert Space MMD, the consistency of the test is proved, under mild assumptions of the kernel, as long as $\|p-q\| \sqrt{n} \to \infty $, and a finite-sample lower bound of the testing power is obtained. Applications to flow cytometry and diffusion MRI datasets are demonstrated, which motivate the proposed approach to compare distributions.
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39

Vansovich, K. A., and V. I. Yadrov. "Biaxial testing of metal samples on testing machines with one load axis." Omsk Scientific Bulletin, no. 173 (2020): 10–16. http://dx.doi.org/10.25206/1813-8225-2020-173-10-16.

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The necessity of experimental studies to assess the growth rate of fatigue cracks under biaxial loading is substantiated. An overview of testing machines for biaxial testing of metal samples, devices and fixtures used to ensure biaxial loading is made. An original device for distributing the vertical force of the testing machine along two axes of a cruciform sample is described
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40

Steuber, Tara L., Peter C. Kiessler, and Robert Lund. "TESTING FOR REVERSIBILITY IN MARKOV CHAIN DATA." Probability in the Engineering and Informational Sciences 26, no. 4 (July 30, 2012): 593–611. http://dx.doi.org/10.1017/s0269964812000228.

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This paper introduces two statistics that assess whether (or not) a sequence sampled from a stationary time-homogeneous Markov chain on a finite state space is reversible. The test statistics are based on observed deviations of transition sample counts between each pair of states in the chain. First, the joint asymptotic normality of these sample counts is established. This result is then used to construct two chi-squared-based tests for reversibility. Simulations assess the power and type one error of the proposed tests.
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41

Havránek, T., and G. A. Lienert. "Remission Control of Pre-Post Treatment Comparisons by Two-Sample Symmetry Testing." Methods of Information in Medicine 25, no. 02 (April 1986): 116–22. http://dx.doi.org/10.1055/s-0038-1635459.

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SummaryStarting from one-sample symmetry testing in pre-post treatment square contingency tables, two-sample symmetry tests were constructed and suggested for control of spontaneous remission in clinical trials. The fourfold tests presented are sensitive to the alternatives of specified improvement effects, global improvement effects and differential improvement effects in subgroups of patients and include a composite fourfold omnibus test which is rather insensitive to many alternatives. The application of two-sample symmetry testing is illustrated numerically by an example from controlled psychotherapy research.
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42

Kim, Sangwoong, and Junmo Song. "Statistical hypothesis testing using deep learning: Focusing on two sample t-test." Journal of the Korean Data And Information Science Society 32, no. 1 (January 31, 2021): 25–35. http://dx.doi.org/10.7465/jkdi.2021.32.1.25.

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43

Arboretti, Rosa, Eleonora Carrozzo, Fortunato Pesarin, and Luigi Salmaso. "A multivariate extension of union–intersection permutation solution for two-sample testing." Journal of Statistical Theory and Practice 11, no. 3 (February 21, 2017): 436–48. http://dx.doi.org/10.1080/15598608.2017.1295891.

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44

Deshpande, J. V., M. Mukhopadhyay, and U. V. Naik-Nimbalkar. "Testing of two sample proportional intensity assumption for non-homogeneous Poisson processes." Journal of Statistical Planning and Inference 81, no. 2 (November 1999): 237–51. http://dx.doi.org/10.1016/s0378-3758(99)00022-1.

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45

Reed, James, and David Stark. "Robust Two-Sample Statistics for Testing Equality of Means: A Simulation Study." Journal of Applied Statistics 31, no. 7 (August 2004): 831–54. http://dx.doi.org/10.1080/0266476042000214529.

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46

Shutoh, Nobumichi, Makiko Kusumi, Wataru Morinaga, Shunichi Yamada, and Takashi Seo. "Testing Equality of Mean Vectors in Two Sample Problem with Missing Data." Communications in Statistics - Simulation and Computation 39, no. 3 (February 24, 2010): 487–500. http://dx.doi.org/10.1080/03610910903480842.

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47

Mellen, Samantha, Maria de Ferrars, Claire Chapman, Sarah Bevan, James Turvill, and Daniel Turnock. "Evaluation of sample stability for a quantitative faecal immunochemical test and comparison of two sample collection approaches." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 55, no. 6 (March 29, 2018): 657–64. http://dx.doi.org/10.1177/0004563218766393.

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Background Faecal immunochemical testing is increasingly being used to triage symptomatic patients for suspected colorectal cancer. However, there are limited data on the effect of preanalytical factors on faecal haemoglobin when measured by faecal immunochemical testing. The aim of this work was to evaluate the stability of faecal haemoglobin in faeces and to compare two methods of faecal haemoglobin sampling for faecal immunochemical testing. Methods Six patients provided faeces for faecal haemoglobin measurement which were transferred into specialized collection devices at baseline and at 1, 2, 3 and 7 days after storage at either room temperature or 4°C. A total of 137 patients returned both faeces transferred into the specialized collection device and faeces in a standard collection pot. A quantitative immunoturbidometric method was used to measure faecal haemoglobin and results were compared categorically. Discrepant results were assessed against diagnosis. Results Faecal haemoglobin concentration declined rapidly within a day of storage at room temperature but results remained ≥10 μg Hb/g faeces in 5/6 patients after two days. A faecal haemoglobin result ≥10 μg Hb/g faeces was obtained in 4/6 patients after storage for seven days at 4°C. Results obtained when patients used specialized collection devices were significantly different from results obtained when faeces was transferred into the specialized collection device in the laboratory. Conclusion There is considerable heterogeneity in the sample stability of faecal haemoglobin; therefore, samples should be transferred rapidly into specialized collection devices to prevent false-negative results. Use of collection devices by patients can lead to false-positive results compared with their use in a laboratory.
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48

Zhang, Rong Fa, Da Yong Shan, and En Hou Han. "Two-Step Anodization of AZ91 Magnesium Alloy." Materials Science Forum 488-489 (July 2005): 653–56. http://dx.doi.org/10.4028/www.scientific.net/msf.488-489.653.

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Among four AZ91 samples, two were once anodized and the others were twice anodized in two electrolytic baths. After twice anodization, two coatings coexist on the base metal. Surface morphology showed that for one of twice anodic coatings (sample 2), the second coating only existed on some places and sealed some pores of the first coating after short time on the second anodizing. However, for another twice anodic coating (namely sample 4), the second anodization spent very long time and the coating was thick and loose. Salt spray testing showed that sample 2 had the most excellent corrosion resistance and the reason is discussed.
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49

Massaad, Georges, Emmanuel Roziere, Ahmed Loukili, and Laurent Izoret. "Advanced External Sulfate Attack Testing and Performance Specifications: Different Sample Geometries Testing." Key Engineering Materials 711 (September 2016): 1045–52. http://dx.doi.org/10.4028/www.scientific.net/kem.711.1045.

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External sulfate attacks induce severe deterioration of concrete because of several phenomena: leaching, precipitation of expansive products, aggregates loss, and cracking. A new methodology has been developed to assess sulfate resistance of cement-based materials. We can actually estimate the nature of leached and precipitated minerals, and the microscopic volume variations due to each of the principal sulfate attack phenomena, from relatively simple monitoring parameters: sample mass, hydrostatic weighing, and the amount of leached hydroxide. Quantitative information is given on the mineralogical volume or mass variations, leading us to a phenomena decoupling and a better understanding of the sulfate attack mechanism. After we established the microscopic volume variation scenario, we defined a new performance criterion, the averaged density, capable to describe the tested sample performance variation according to the sample internal state indications given by this criterion. We applied the developed strategy on three different filled and hollow geometries in order to study the effect of the sample geometry on sulfate attack mechanism. The study shows almost the same degradation mechanism: the same microscopic volume variations entrained by the different sulfate attack phenomena. However the hollow tubes promote different precipitated mineral than the two others geometries, due to the different ionic and pH profiles between filled and hollow samples which affects the nature of formed sulfate minerals.
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50

Krzyśko, Mirosław, and Łukasz Smaga. "Two-sample Tests for Functional Data Using Characteristic Functions." Austrian Journal of Statistics 50, no. 4 (July 15, 2021): 53–64. http://dx.doi.org/10.17713/ajs.v50i4.1099.

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In this paper, we consider the two-sample problem for univariate and multivariate functional data. To solve this problem, we use tool of characteristic function and a basis function representation of functional data. We construct test statistics for conformity of distributions based on a weighted distance between characteristic functions of random vectors obtained in basis representation. Different weight functions result in different test statistics, whose distributions are approximated by permutation method. Testing procedures are implemented in the R program and the code is available. Simulation study shows good finite sample properties of proposed methods, while real data example illustrates the application of them.
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