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Journal articles on the topic 'Low sample size'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Litwin, Samuel, Stanley Basickes, and Eric A. Ross. "Two-sample binary phase 2 trials with low type I error and low sample size." Statistics in Medicine 36, no. 9 (2017): 1383–94. http://dx.doi.org/10.1002/sim.7226.

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2

Litwin, Samuel, Eric Ross, and Stanley Basickes. "Two-sample binary phase 2 trials with low type I error and low sample size." Statistics in Medicine 36, no. 21 (2017): 3439. http://dx.doi.org/10.1002/sim.7358.

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3

Alban, Eduardo X., Mario E. Magaña, and Harry Skinner. "A Low Sample Size Estimator for K Distributed Noise." Journal of Signal and Information Processing 03, no. 03 (2012): 293–307. http://dx.doi.org/10.4236/jsip.2012.33039.

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4

Jung, Sungkyu, and J. S. Marron. "PCA consistency in high dimension, low sample size context." Annals of Statistics 37, no. 6B (2009): 4104–30. http://dx.doi.org/10.1214/09-aos709.

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5

Zhou, Yi-Hui, and J. S. Marron. "High dimension low sample size asymptotics of robust PCA." Electronic Journal of Statistics 9, no. 1 (2015): 204–18. http://dx.doi.org/10.1214/15-ejs992.

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6

Aoshima, Makoto, and Kazuyoshi Yata. "Statistical inference for high-dimension, low-sample-size data." Sugaku Expositions 30, no. 2 (2017): 137–58. http://dx.doi.org/10.1090/suga/421.

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7

Hall, Peter, J. S. Marron, and Amnon Neeman. "Geometric representation of high dimension, low sample size data." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, no. 3 (2005): 427–44. http://dx.doi.org/10.1111/j.1467-9868.2005.00510.x.

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8

Aoshima, Makoto, Dan Shen, Haipeng Shen, Kazuyoshi Yata, Yi-Hui Zhou, and J. S. Marron. "A survey of high dimension low sample size asymptotics." Australian & New Zealand Journal of Statistics 60, no. 1 (2018): 4–19. http://dx.doi.org/10.1111/anzs.12212.

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9

Shan, Guogen. "Comments on ‘Two-sample binary phase 2 trials with low type I error and low sample size’." Statistics in Medicine 36, no. 21 (2017): 3437–38. http://dx.doi.org/10.1002/sim.7359.

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Sarkar, Soham, Rahul Biswas, and Anil K. Ghosh. "On some graph-based two-sample tests for high dimension, low sample size data." Machine Learning 109, no. 2 (2019): 279–306. http://dx.doi.org/10.1007/s10994-019-05857-4.

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11

Kuncheva, Ludmila I., and Juan J. Rodríguez. "On feature selection protocols for very low-sample-size data." Pattern Recognition 81 (September 2018): 660–73. http://dx.doi.org/10.1016/j.patcog.2018.03.012.

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12

Sarkar, Soham, and Anil K. Ghosh. "On Perfect Clustering of High Dimension, Low Sample Size Data." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 9 (2020): 2257–72. http://dx.doi.org/10.1109/tpami.2019.2912599.

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13

Sen, Pranab K., Ming-Tien Tsai, and Yuh-Shan Jou. "High-Dimension, Low–Sample Size Perspectives in Constrained Statistical Inference." Journal of the American Statistical Association 102, no. 478 (2007): 686–94. http://dx.doi.org/10.1198/016214507000000077.

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14

Cheema, Muhammad Shahzad, Abdalrahman Eweiwi, and Christian Bauckhage. "High dimensional low sample size activity recognition using geometric classifiers." Digital Signal Processing 42 (July 2015): 61–69. http://dx.doi.org/10.1016/j.dsp.2015.03.019.

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15

Boddy, Clive Roland. "Sample size for qualitative research." Qualitative Market Research: An International Journal 19, no. 4 (2016): 426–32. http://dx.doi.org/10.1108/qmr-06-2016-0053.

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Purpose Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research. Design/methodology/approach The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years. Findings In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population. Practical implications Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research. Originality/value Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

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16

Jung, Sungkyu, Arusharka Sen, and J. S. Marron. "Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA." Journal of Multivariate Analysis 109 (August 2012): 190–203. http://dx.doi.org/10.1016/j.jmva.2012.03.005.

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17

Shen, Dan, Haipeng Shen, and J. S. Marron. "Consistency of sparse PCA in High Dimension, Low Sample Size contexts." Journal of Multivariate Analysis 115 (March 2013): 317–33. http://dx.doi.org/10.1016/j.jmva.2012.10.007.

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18

Zhang, Lingsong, and Xihong Lin. "Some considerations of classification for high dimension low-sample size data." Statistical Methods in Medical Research 22, no. 5 (2011): 537–50. http://dx.doi.org/10.1177/0962280211428387.

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19

Liu, Yufeng, David Neil Hayes, Andrew Nobel, and J. S. Marron. "Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data." Journal of the American Statistical Association 103, no. 483 (2008): 1281–93. http://dx.doi.org/10.1198/016214508000000454.

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20

Lu, Qiyi, and Xingye Qiao. "Significance analysis of high-dimensional, low-sample size partially labeled data." Journal of Statistical Planning and Inference 176 (September 2016): 78–94. http://dx.doi.org/10.1016/j.jspi.2016.03.002.

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21

Chan, Y. B., and P. Hall. "Scale adjustments for classifiers in high-dimensional, low sample size settings." Biometrika 96, no. 2 (2009): 469–78. http://dx.doi.org/10.1093/biomet/asp007.

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22

Song, Juhee, and Jeffrey D. Hart. "Bootstrapping in a high dimensional but very low-sample size problem." Journal of Statistical Computation and Simulation 80, no. 8 (2010): 825–40. http://dx.doi.org/10.1080/00949650902798129.

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23

Ahn, Jeongyoun, Myung Hee Lee, and Jung Ae Lee. "Distance-based outlier detection for high dimension, low sample size data." Journal of Applied Statistics 46, no. 1 (2018): 13–29. http://dx.doi.org/10.1080/02664763.2018.1452901.

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24

Christoph, Gerd, and Vladimir V. Ulyanov. "Second Order Expansions for High-Dimension Low-Sample-Size Data Statistics in Random Setting." Mathematics 8, no. 7 (2020): 1151. http://dx.doi.org/10.3390/math8071151.

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We consider high-dimension low-sample-size data taken from the standard multivariate normal distribution under assumption that dimension is a random variable. The second order Chebyshev–Edgeworth expansions for distributions of an angle between two sample observations and corresponding sample correlation coefficient are constructed with error bounds. Depending on the type of normalization, we get three different limit distributions: Normal, Student’s t-, or Laplace distributions. The paper continues studies of the authors on approximation of statistics for random size samples.

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25

Zedaker, S. M., T. G. Gregoire, and J. H. Miller. "Sample-size needs for forestry herbicide trials." Canadian Journal of Forest Research 23, no. 10 (1993): 2153–58. http://dx.doi.org/10.1139/x93-268.

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Forest herbicide experiments are increasingly being designed to evaluate smaller treatment differences when comparing existing effective treatments, tank mix ratios, surfactants, and new low-rate products. The ability to detect small differences in efficacy is dependent upon the relationship among sample size, type I and II error probabilities, and the coefficients of variation of the efficacy data. The common sources of variation in efficacy measurements and design considerations for controlling variation are reviewed, while current shortcomings are clarified. A summary of selected trials estimates that coefficients of variation often range between 25 and 100%, making the number of observations necessary to detect small differences very large, especially when the power of the test (1–β) is considered. Very often the power of the test has been ignored when designing experiments because of the difficulty in calculating β. An available program for microcomputers is introduced that allows researchers to examine relationships among sample size, effect size, and coefficients of variation for specified designs, α and β. This program should aid investigators in planning studies that optimize experimental power to detect anticipated effect sizes within resource constraints.

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26

Bolivar-Cime, A., and J. S. Marron. "Comparison of binary discrimination methods for high dimension low sample size data." Journal of Multivariate Analysis 115 (March 2013): 108–21. http://dx.doi.org/10.1016/j.jmva.2012.10.001.

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27

Yin, Qingbo, Ehsan Adeli, Liran Shen, and Dinggang Shen. "Population-guided large margin classifier for high-dimension low-sample-size problems." Pattern Recognition 97 (January 2020): 107030. http://dx.doi.org/10.1016/j.patcog.2019.107030.

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28

Becker, W. E., S. Tarantola, and G. Deman. "Sensitivity analysis approaches to high-dimensional screening problems at low sample size." Journal of Statistical Computation and Simulation 88, no. 11 (2018): 2089–110. http://dx.doi.org/10.1080/00949655.2018.1450876.

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29

Ahn, J., J. S. Marron, K. M. Muller, and Y. Y. Chi. "The high-dimension, low-sample-size geometric representation holds under mild conditions." Biometrika 94, no. 3 (2007): 760–66. http://dx.doi.org/10.1093/biomet/asm050.

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30

Simpson, Sean L., Lloyd J. Edwards, Martin A. Styner, and Keith E. Muller. "Separability tests for high-dimensional, low-sample size multivariate repeated measures data." Journal of Applied Statistics 41, no. 11 (2014): 2450–61. http://dx.doi.org/10.1080/02664763.2014.919251.

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31

Yata, Kazuyoshi, and Makoto Aoshima. "Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics." Communications in Statistics - Theory and Methods 39, no. 8-9 (2010): 1511–21. http://dx.doi.org/10.1080/03610920903121999.

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32

von Borries, George, and Haiyan Wang. "Partition clustering of high dimensional low sample size data based on -values." Computational Statistics & Data Analysis 53, no. 12 (2009): 3987–98. http://dx.doi.org/10.1016/j.csda.2009.06.012.

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33

Marozzi, Marco. "Multivariate multidistance tests for high-dimensional low sample size case-control studies." Statistics in Medicine 34, no. 9 (2015): 1511–26. http://dx.doi.org/10.1002/sim.6418.

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34

Ishii, Aki. "A two-sample test for high-dimension, low-sample-size data under the strongly spiked eigenvalue model." Hiroshima Mathematical Journal 47, no. 3 (2017): 273–88. http://dx.doi.org/10.32917/hmj/1509674448.

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35

Pitkänen, Leena, and Herbert Sixta. "Size-exclusion chromatography of cellulose: observations on the low-molar-mass fraction." Cellulose 27, no. 16 (2020): 9217–25. http://dx.doi.org/10.1007/s10570-020-03419-9.

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AbstractAccurate determination of molar mass distribution for disperse cellulose samples has proved to be a challenging task. While size-exclusion chromatography coupled to multi-angle light scattering (MALS) and differential refractive index (DRI) detectors has become the most commonly used method for molar mass determination of celluloses, this technique suffers low sensitivity at the low-molar mass range. As discussed here, the universal method for accurate molar mass distribution analysis of cellulose samples not exists and thus thorough understanding on the differences of the various methodological approaches is important. In this study, the focus is in the accurate determination of the low-molar mass fraction. The results obtained by combining the two calibration strategies, MALS/DRI for polymeric region of a cellulose sample and conventional calibration for oligomeric region, was compared to the results obtained using only MALS/DRI (with extrapolation of the curve where signal-to-noise of MALS is low). For birch pulp sample, the results from the two approaches were comparable; it should be highlighted, however, that MALS/DRI slightly overestimates the molar masses at the low-molar-mass region.

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36

Bell, Bethany A., Grant B. Morgan, Jason A. Schoeneberger, Jeffrey D. Kromrey, and John M. Ferron. "How Low Can You Go?" Methodology 10, no. 1 (2014): 1–11. http://dx.doi.org/10.1027/1614-2241/a000062.

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Whereas general sample size guidelines have been suggested when estimating multilevel models, they are only generalizable to a relatively limited number of data conditions and model structures, both of which are not very feasible for the applied researcher. In an effort to expand our understanding of two-level multilevel models under less than ideal conditions, Monte Carlo methods, through SAS/IML, were used to examine model convergence rates, parameter point estimates (statistical bias), parameter interval estimates (confidence interval accuracy and precision), and both Type I error control and statistical power of tests associated with the fixed effects from linear two-level models estimated with PROC MIXED. These outcomes were analyzed as a function of: (a) level-1 sample size, (b) level-2 sample size, (c) intercept variance, (d) slope variance, (e) collinearity, and (f) model complexity. Bias was minimal across nearly all conditions simulated. The 95% confidence interval coverage and Type I error rate tended to be slightly conservative. The degree of statistical power was related to sample sizes and level of fixed effects; higher power was observed with larger sample sizes and level-1 fixed effects.

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37

Williams, Michael S., Eric D. Ebel, and Bruce A. Wagner. "Monte Carlo approaches for determining power and sample size in low-prevalence applications." Preventive Veterinary Medicine 82, no. 1-2 (2007): 151–58. http://dx.doi.org/10.1016/j.prevetmed.2007.05.015.

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38

Chow, Shein-Chung, and Shih-Ting Chiu. "Sample Size and Data Monitoring for Clinical Trials With Extremely Low Incidence Rates." Therapeutic Innovation & Regulatory Science 47, no. 4 (2013): 438–46. http://dx.doi.org/10.1177/2168479013489298.

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39

Yata, Kazuyoshi, and Makoto Aoshima. "PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context." Communications in Statistics - Theory and Methods 38, no. 16-17 (2009): 2634–52. http://dx.doi.org/10.1080/03610910902936083.

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40

Ribeiro-Oliveira, João Paulo, and Marli A. Ranal. "Sample size in studies on the germination process." Botany 94, no. 2 (2016): 103–15. http://dx.doi.org/10.1139/cjb-2015-0161.

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Studies on diaspore germination in native species with low economic relevance but great ecological significance have been based on a wide range of sample sizes. However, can the sample size change the physiological inferences made from germination measurements? To answer this question, diaspores of six Cerrado species were evaluated for germinability, germination time (initial, mean, and final), germination velocity (mean germination rate and Maguire’s rate), coefficient of variation of the germination time, and synchronization index of the germination process. Germinability, final time, mean time, and synchronization index were robust with respect to sample size fluctuation. Maguire’s rate, initial time, coefficient of variation of the germination time, and mean germination rate, in contrast, were affected by sample size fluctuation, at least in one of the species tested. The robustness of the time measurements and the synchronization index also demonstrates that the germination process occurs in a cadenced rhythm, much like a biological clock. Among the measurements evaluated, Maguire’s rate is the only one that must be avoided, since it is strongly influenced by sample size and by the balance between germinability and mean germination rate. These results demonstrate that sample size can affect inferences about the germination process and can compromise restoration and (or) conservation efforts.

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41

Blair, R. Clifford, and James J. Higgins. "A Comparison of the Power of the Paired Samples Rank Transform Statistic to that of Wilcoxon’s Signed Ranks Statistic." Journal of Educational Statistics 10, no. 4 (1985): 368–83. http://dx.doi.org/10.3102/10769986010004368.

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This study was concerned with the effects of reliability of observations, sample size, magnitudes of treatment effects, and the shape of the sampled population on the relative power of the paired samples rank transform statistic and Wilcoxon’s signed ranks statistic. It was found that factors favoring the Wilcoxon statistic were high reliability of observations, moderate to large sample sizes, and small treatment effects. Factors favoring the rank transform statistic were low reliability of observations, small sample size, and moderate to large treatment effects. It was also noted that the Wilcoxon statistic appeared to maintain the power advantage under normal theory assumptions.

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42

Schabarum, Denison Esequiel, Alberto Cargnelutti Filho, André Lavezo, et al. "Sample Size for Morphological Traits of Sunn Hemp." Journal of Agricultural Science 10, no. 1 (2017): 152. http://dx.doi.org/10.5539/jas.v10n1p152.

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Sunn hemp (Crotalaria juncea L.) is an annual leguminous plant used for crop rotation, biomass formation, biological nitrogen fixation, and nematode control. To this end, sampling is an important tool owing to its advantages, such as low cost and fast data acquisition. This study aimed to determine the sample size required to estimate the mean morphological traits of sunn hemp and to assess sample size variability among traits, crop development stages, and sowing seasons. Two uniformity trials were performed in the field during the 2014/2015 harvest. Crops were sown in October and December on an experimental area of the Department of Crop Science at the Federal University of Santa Maria, Brazil. An area of 1,200 m2 was allocated for each trial. Each trial was divided into 2 m × 2 m grids, which formed 25 rows and 12 columns. One plant was marked per plot, totaling 300 plants in each trial. Leaf number and plant height were assessed weekly. Stem diameter and root length were measured at flowering. Normality and randomness tests were performed on each trait. The sample sizes (plants number) were calculated for the confidence interval half-widths (estimation errors) of the 2, 4, …, 20% mean estimates. There is variability of the sample size (plants number) between morphological traits, crop development stages, and sowing seasons. When choosing a single sample size to evaluate the morphological traits of sunn hemp, at least 70 plants should be sampled for an accuracy of 10% and a 95% confidence level.

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43

Militzer, Matthias, Mehran Maalekian, and André Moreau. "Laser-Ultrasonic Austenite Grain Size Measurements in Low-Carbon Steels." Materials Science Forum 715-716 (April 2012): 407–14. http://dx.doi.org/10.4028/www.scientific.net/msf.715-716.407.

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Austenite grain size is an important microstructure parameter when processing steels as it provides the initial condition for the austenite decomposition that determines the final microstructure and thus properties of the steel. In low-carbon steels it is frequently difficult if not impossible to quantify the austenite grain size using conventional metallographic techniques. Laser-ultrasonics provides an attractive alternative to quantify the grain size in-situ during thermo-mechanical processing of a steel sample. The attenuation of the laser generated ultrasound wave is a function of the grain size. The present paper gives an overview on the state-of-the-art of this novel measurement technique. Using isothermal and non-isothermal grain growth tests in low-carbon steels the advantages and limitations of laser-ultrasonic measurements will be demonstrated. Further, their application for deformed samples will be presented to quantify austenite grain sizes during and after recrystallization. The in-situ measurements provide significantly new insights into the austenite microstructure evolution during thermo-mechanical processing of low-carbon steels. The implications on expediting the development of improved process models will be discussed.

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44

Tamatani, Mitsuru, Kanta Naito, and Inge Koch. "MULTI-CLASS DISCRIMINANT FUNCTION BASED ON CANONICAL CORRELATION IN HIGH DIMENSION LOW SAMPLE SIZE." Bulletin of informatics and cybernetics 45 (December 2013): 67–101. http://dx.doi.org/10.5109/1563533.

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45

Shen, Liran, and Qingbo Yin. "Data maximum dispersion classifier in projection space for high-dimension low-sample-size problems." Knowledge-Based Systems 193 (April 2020): 105420. http://dx.doi.org/10.1016/j.knosys.2019.105420.

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46

Tamatani, Mitsuru, Inge Koch, and Kanta Naito. "Pattern recognition based on canonical correlations in a high dimension low sample size context." Journal of Multivariate Analysis 111 (October 2012): 350–67. http://dx.doi.org/10.1016/j.jmva.2012.04.011.

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47

Nakayama, Yugo, Kazuyoshi Yata, and Makoto Aoshima. "Support vector machine and its bias correction in high-dimension, low-sample-size settings." Journal of Statistical Planning and Inference 191 (December 2017): 88–100. http://dx.doi.org/10.1016/j.jspi.2017.05.005.

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48

Dutta, Subhajit, and Anil K. Ghosh. "On some transformations of high dimension, low sample size data for nearest neighbor classification." Machine Learning 102, no. 1 (2015): 57–83. http://dx.doi.org/10.1007/s10994-015-5495-y.

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49

Wang, Zesong, Cui Zou, and Xianping Cui. "Low-sample size remote sensing image recognition based on a multihead attention integration network." Multimedia Tools and Applications 79, no. 43-44 (2020): 32525–40. http://dx.doi.org/10.1007/s11042-020-09641-8.

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50

McClymont, Juliet, Russell Savage, Todd C. Pataky, Robin Crompton, James Charles, and Karl T. Bates. "Intra-subject sample size effects in plantar pressure analyses." PeerJ 9 (June 24, 2021): e11660. http://dx.doi.org/10.7717/peerj.11660.

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Background Recent work using large datasets (>500 records per subject) has demonstrated seemingly high levels of step-to-step variation in peak plantar pressure within human individuals during walking. One intuitive consequence of this variation is that smaller sample sizes (e.g., 10 steps per subject) may be quantitatively and qualitatively inaccurate and fail to capture the variance in plantar pressure of individuals seen in larger data sets. However, this remains quantitatively unexplored reflecting a lack of detailed investigation of intra-subject sample size effects in plantar pressure analysis. Methods Here we explore the sensitivity of various plantar pressure metrics to intra-subject sample size (number of steps per subject) using a random subsampling analysis. We randomly and incrementally subsample large data sets (>500 steps per subject) to compare variability in three metric types at sample sizes of 5–400 records: (1) overall whole-record mean and maximum pressure; (2) single-pixel values from five locations across the foot; and (3) the sum of pixel-level variability (measured by mean square error, MSE) from the whole plantar surface. Results Our results indicate that the central tendency of whole-record mean and maximum pressure within and across subjects show only minor sensitivity to sample size >200 steps. However, <200 steps, and particularly <50 steps, the range of overall mean and maximum pressure values yielded by our subsampling analysis increased considerably resulting in potential qualitative error in analyses of pressure changes with speed within-subjects and in comparisons of relative pressure magnitudes across subjects at a given speed. Our analysis revealed considerable variability in the absolute and relative response of the single pixel centroids of five regions to random subsampling. As the number of steps analysed decreased, the absolute value ranges were highest in the areas of highest pressure (medial forefoot and hallux), while the largest relative changes were seen in areas of lower pressure (the midfoot). Our pixel-level measure of variability by MSE across the whole-foot was highly sensitive to our manipulation of sample size, such that the range in MSE was exponentially larger in smaller subsamples. Random subsampling showed that the range in pixel-level MSE only came within 5% of the overall sample size in subsamples of >400 steps. The range in pixel-level MSE at low subsamples (<50) was 25–75% higher than that of the full datasets of >500 pressure records per subject. Overall, therefore, we demonstrate a high probability that the very small sample sizes (n < 20 records), which are routinely used in human and animal studies, capture a relatively low proportion of variance evident in larger plantar pressure data set, and thus may not accurately reflect the true population mean.

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