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Journal articles on the topic 'Parametric and nonparametric tests'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

HARWELL, MICHAEL R. "Choosing Between Parametric and Nonparametric Tests." Journal of Counseling & Development 67, no. 1 (September 1988): 35–38. http://dx.doi.org/10.1002/j.1556-6676.1988.tb02007.x.

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2

Stojanović, Miodrag, Marija Andjelković - Apostolović, Zoran Milošević, and Aleksandra Ignjatović. "PARAMETRIC VERSUS NONPARAMETRIC TESTS IN BIOMEDICAL RESEARCH." Acta Medica Medianae 57, no. 2 (March 15, 2018): 75–80. http://dx.doi.org/10.5633/amm.2018.0212.

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3

Hunter, Michael A., and Richard B. May. "Some myths concerning parametric and nonparametric tests." Canadian Psychology/Psychologie canadienne 34, no. 4 (1993): 384–89. http://dx.doi.org/10.1037/h0078860.

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4

El-Shaarawi, A. H., and Eivind Damsleth. "PARAMETRIC AND NONPARAMETRIC TESTS FOR DEPENDENT DATA." Journal of the American Water Resources Association 24, no. 3 (June 1988): 513–19. http://dx.doi.org/10.1111/j.1752-1688.1988.tb00901.x.

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5

Cintra, Renato J., Alejandro C. Frery, and Abraão D. C. Nascimento. "Parametric and nonparametric tests for speckled imagery." Pattern Analysis and Applications 16, no. 2 (October 15, 2011): 141–61. http://dx.doi.org/10.1007/s10044-011-0249-3.

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6

Blanciforti, Laura A. "Consumer Demand Analysis According to GARP: Discussion." Northeastern Journal of Agricultural and Resource Economics 21, no. 2 (October 1992): 140–41. http://dx.doi.org/10.1017/s0899367x00002658.

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This paper explores the issue of the power of nonparametric tests to check for the consistency of data with utility maximization. Alston and Chalfant provide an excellent review of nonparametric approaches to consumer-demand analysis. They test for consistency, separability, and power. The authors address two important questions: First, how does one define power for nonparametric situations, and is that definition comparable to the parametric situation? Second, can the power of nonparametric tests be improved? The authors measure the statistical power of nonparametric methods using a parametric test, though they do not address whether it is legitimate to use parametric tests on nonparametric methods.

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7

Singhal, Richa, RakeshKumar Rana, and Pamila Dua. "Deciphering the dilemma of parametric and nonparametric tests." Journal of the Practice of Cardiovascular Sciences 2, no. 2 (2016): 95. http://dx.doi.org/10.4103/2395-5414.191521.

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8

Kahraman, Cengiz, Cafer Erhan Bozdag, Da Ruan, and Ahmet Fahri Özok. "Fuzzy sets approaches to statistical parametric and nonparametric tests." International Journal of Intelligent Systems 19, no. 11 (September 24, 2004): 1069–87. http://dx.doi.org/10.1002/int.20037.

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9

Emerson, Robert Wall. "Parametric Tests, Their Nonparametric Alternatives, and Degrees of Freedom." Journal of Visual Impairment & Blindness 110, no. 5 (September 2016): 377–80. http://dx.doi.org/10.1177/0145482x1611000511.

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10

Kitchen, Christina M. R. "Nonparametric vs Parametric Tests of Location in Biomedical Research." American Journal of Ophthalmology 147, no. 4 (April 2009): 571–72. http://dx.doi.org/10.1016/j.ajo.2008.06.031.

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11

Zimmerman, Donald W. "Note on the Influence of Distribution Shape on Nonparametric Tests." Perceptual and Motor Skills 79, no. 3 (December 1994): 1160–62. http://dx.doi.org/10.2466/pms.1994.79.3.1160.

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The Wilcoxon-Mann-Whitney test and other nonparametric tests based on rank randomization are distribution-free with respect to the probability of Type I errors; however, they are not distribution-free with respect to the probability of Type II errors and power to detect differences. Various shapes of nonnormal distributions substantially reduce the power of nonparametric tests. However, these densities influence parametric counterparts such as t and F to an even greater extent so nonparametric methods acquire an advantage by comparison.

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12

Zimmerman, Donald W. "A Note on Nonindependence and Nonparametric Tests." Perceptual and Motor Skills 76, no. 2 (April 1993): 407–12. http://dx.doi.org/10.2466/pms.1993.76.2.407.

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This study investigated violations of random sampling and random assignment in data analyzed by nonparametric significance tests. A computer program induced correlations within groups, as well as between groups, and performed one-sample and two-sample versions of the Mann-Whitney-Wilcoxon test on the resulting scores. Nonindependence of observations within groups spuriously inflated the probability of Type I errors and depressed the probability of Type II errors, and nonindependence between groups had the reverse effect. This outcome, which parallels the influence of nonindependence on parametric tests, can be explained by the equivalence of the Mann-Whitney-Wilcoxon test and the Student t test performed on ranks replacing the initial scores.

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13

Koshmak, Viktor, Aleksandr Hvatcev, Ina Astahova, and Aleksandr Zuev. "SAMPLES DISTINCTION BY PARAMETRIC AND NONPARAMETRIC STATISTICS IN SPSS." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 5 (May 21, 2019): 374. http://dx.doi.org/10.17770/sie2019vol5.3779.

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Testing samples distinction is necessary in a wide range of practical tasks. Medicine, sociology, psychology, marketing - this is a short list of industries where it is required to conduct tests that establish effectiveness or inefficiency of a certain technology.Diversity of situations and techniques applied to sample distinction create a problem for compliance of testing procedures. The problem rises for tests including large and small samples (dependent or independent) with various distributions. The article proposes a list of problems created by testing differences between two samples. Limits of applicability of parametric and non-parametric tests are established based on selected distribution. Informative examples are included based on simulated data.SPSS software was used for sample distinction tests. It is important to double-check the operation of the machine computing procedure "manually” to understand the nature of tests and in educational purposes. The article provides mathematical illustration for the algorithms used, which can be considered as supplementary information for SPSS help.

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14

Bentarzi, Mohamed, and Marc Hallin. "Locally Optimal Tests against Periodic Autoregression: Parametric and Nonparametric Approaches." Econometric Theory 12, no. 1 (March 1996): 88–112. http://dx.doi.org/10.1017/s0266466600006459.

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Locally asymptotically optimal tests are derived for the null hypothesis of traditional AR dependence, with unspecified AR coefficients and unspecified innovation densities, against an alternative of periodically correlated AR dependence. Parametric and nonparametric rank-based versions are proposed. Local powers and asymptotic relative efficiencies (with respect, e.g., to the corresponding Gaussian Lagrange multiplier tests proposed in Ghysels and Hall [1992, “Lagrange Multiplier Tests for Periodic Structures,” unpublished manuscript, CRDE, Montreal] and Liitkepohl [1991, Introduction to Multiple Time Series Analysis, Berlin: Springer-Verlag; 1991, pp. 243–264, in W.E. Griffiths, H. Liitkepohl, & M.E. Block (eds.), Readings in Econometric Theory and Practice, Amsterdam: North-Holland] are computed explicitly; a rank-based test of the van der Waerden type is proposed, for which this ARE is uniformly larger than 1. The main technical tool is Le Cam's local asymptotic normality property.

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15

Qualls, Munirih, Daniel J. Pallin, and Jeremiah D. Schuur. "Parametric Versus Nonparametric Statistical Tests: The Length of Stay Example." Academic Emergency Medicine 17, no. 10 (October 2010): 1113–21. http://dx.doi.org/10.1111/j.1553-2712.2010.00874.x.

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16

Muhammad, Noryanti, F. P. A. Coolen, and T. Coolen-Maturi. "Nonparametric predictive inference for combining diagnostic tests with parametric copula." Journal of Physics: Conference Series 890 (September 2017): 012129. http://dx.doi.org/10.1088/1742-6596/890/1/012129.

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17

Su, Liangjun, and Aman Ullah. "A NONPARAMETRIC GOODNESS-OF-FIT-BASED TEST FOR CONDITIONAL HETEROSKEDASTICITY." Econometric Theory 29, no. 1 (July 6, 2012): 187–212. http://dx.doi.org/10.1017/s0266466612000278.

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In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R2) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p-values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in the literature.

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18

Hageman, Steven J. "Alternative methods for dealing by nonnormality and heteroscedasticity in paleontological data." Journal of Paleontology 66, no. 6 (November 1992): 857–67. http://dx.doi.org/10.1017/s0022336000020989.

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Although numerical methods are highly useful in paleontological studies, potential problems arise with application of parametric statistical methods to paleontological data. Most common statistical tests assume data are normally distributed and that multiple populations have equal variances (homoscedasticity). Paleontological data frequently do not satisfy these assumptions, thereby affecting results of tests and potentially misleading scientific interpretations. Nonparametric tests should be used when assumptions of parametric tests are violated. Normal scores tests, which utilize expected normal deviates (rankits) substituted for original data, are the most powerful nonparametric tests. Despite their potential utility, normal scores tests have received little attention, primarily because of difficulties encountered with rankit conversion.Recent advances in microcomputer technology provide viable methods for rankit conversion, thus making normal scores tests accessible for routine application. Normal scores tests provide a practical method of dealing with nonnormality and heteroscedasticity common in paleontological data.

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19

Mukhopadhyay, Indranil. "Non-Parametric Tests for Multiple Regression Under Permutation Symmetry." Calcutta Statistical Association Bulletin 38, no. 1-2 (March 1989): 93–114. http://dx.doi.org/10.1177/0008068319890108.

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In this paper non-parametric tests for the multiple resrcssion set up which take into account the known permutation symmetry of the variates under the null hypothesis, are suggested. It bas been shown that under permutation symmetry the proposed procedure is more efficient than the standard nonparametric procedure for the multiple regression problem (see e.g. Puri and Sen (1985) ). A special situation where it is further known in advance that the regressions are identical is also considered briefly.

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20

Martinenko, Anastasija, Vesna Jevremović, Petko Vranić, Jovan Popović, and Marko Pejić. "Statistical tests and their application in geodesy." Tehnika 76, no. 2 (2021): 147–54. http://dx.doi.org/10.5937/tehnika2102147m.

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The correct conclusion about the assumptions concerning some phenomena can be obtained only through scientific analysis of statistical data. The scientific procedure of verifying a hypothesis using measurement results is called a statistical test. Depending on whether the hypotheses about the parameters in the feature distribution are tested or the distribution as a whole is tested, a parametric or non-parametric test is selected. The most significant representatives of parametric tests are the Probability Ratio Test, the Neumann - Pearson Lemma and the Bootstrap method, while the Pearson x2 test and the Kolmogorov test are presented as representatives of nonparametric tests. The paper presents the theoretical basis of some methods used in construction of statistical tests with given examples in geodesy.

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21

Sham, Pak Chung, Ming-Wei Lin, Jing Hua Zhao, and David Curtis. "Power Comparison of Parametric and Nonparametric Linkage Tests in Small Pedigrees." American Journal of Human Genetics 66, no. 5 (May 2000): 1661–68. http://dx.doi.org/10.1086/302888.

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22

Hopkins, Sarah, Joseph R. Dettori, and Jens R. Chapman. "Parametric and Nonparametric Tests in Spine Research: Why Do They Matter?" Global Spine Journal 8, no. 6 (June 12, 2018): 652–54. http://dx.doi.org/10.1177/2192568218782679.

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23

Turechek, W. W. "Nonparametric Tests in Plant Disease Epidemiology: Characterizing Disease Associations." Phytopathology® 94, no. 9 (September 2004): 1018–21. http://dx.doi.org/10.1094/phyto.2004.94.9.1018.

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Nonparametric tests are suited to many statistical applications, including experimental design, regression, and time series analysis, for example. Often these tests are thought of as alternatives to their parametric counterparts when certain assumptions about the underlying population are questionable. Although suited for this scenario, there are a number of nonparametric tests that fill unique niches in the analysis of data, for example, characterizing interspecific associations. Quantifying the degree of association between two or more pathogens or diseases at a defined spatial scale is essential to gain a thorough understanding of disease dynamics, generate testable hypothesis behind the mechanisms that cause association, and is often necessary in modeling applications. In this paper, nonparametric approaches to characterizing interspecific associations will be covered. Specifically, I will address the use of rank correlation coefficients and the development of a randomization procedure for testing the Jaccard index of association against a null model.

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24

Hidalgo, Javier. "A Nonparametric Conditional Moment Test for Structural Stability." Econometric Theory 11, no. 4 (August 1995): 671–98. http://dx.doi.org/10.1017/s0266466600009683.

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This paper considers a nonparametric conditional moment test of stability of an econometric model against the alternative of instability. The alternative hypothesis allows for more than one structural change, although in this case it has to be fairly smooth. This complements existing results for stability in a parametric setting. Also, it is shown that the test is always consistent, unlike the available “parametric” tests, which normally rely on the assumption of a correct specification of the model, at least under the null hypothesis of no structural instability. Moreover, we show that the test has local power comparable to the parametric ones; that is, its asymptotic efficiency is greater than zero. A Monte Carlo experiment about the performance of our test is described.

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25

Wen, Kuangyu, and Ximing Wu. "A guided nonparametric goodness-of-fit test with application to income distributions." Econometrics Journal 22, no. 3 (May 22, 2019): 207–22. http://dx.doi.org/10.1093/ectj/utz007.

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Summary We have developed a customizable goodness-of-fit test of a parametric density based on its distance to a consistently estimated density. This consistent estimate is obtained via a nonparametric density estimator with a parametric start, wherein the start is set to be the hypothesized parametric density. To cope with the influence of nonparametric estimation bias, nonparametric goodness-of-fit tests have resorted to remedies such as undersmoothing or convolution of the hypothesized density. Our test requires no such devices and possesses enhanced powers against alternative densities because the guided density estimator is free of the typical nonparametric bias under the null hypothesis and attains bias reduction when the underlying density is in a broad nonparametric neighborhood of the hypothesized density. Here, we establish the statistical properties of our test and use Monte Carlo simulations to demonstrate its finite sample performance. We use this test to examine the goodness-of-fit of normal mixtures to the distributions of log income of U.S. states. Although normality is rejected decisively, our results suggest that normal mixtures with two or three components suffice for all but one state.

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26

Zimmerman, Donald W. "A Note on the Influence of Outliers on Parametric and Nonparametric Tests." Journal of General Psychology 121, no. 4 (October 1994): 391–401. http://dx.doi.org/10.1080/00221309.1994.9921213.

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27

Zimmerman, Donald W., and Bruno D. Zumbo. "Parametric Alternatives to the Student T Test under Violation of Normality and Homogeneity of Variance." Perceptual and Motor Skills 74, no. 3 (June 1992): 835–44. http://dx.doi.org/10.2466/pms.1992.74.3.835.

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Introductory statistics textbooks in psychology, education, and social sciences have contributed to the belief that nonparametric tests, such as the Wilcoxon-Mann-Whitney test, are effective against violations of both normality and homogeneity of variance. The present paper emphasizes that, although rank methods often are useful when samples are obtained from heavy-tailed, nonnormal distributions, they are influenced by unequal variances just like parametric tests. Computer programs are now available to perform modified t tests based on unequal sample variances, in which degrees of freedom and critical values are altered from sample to sample. These procedures, although neglected for many years because they are computationally complex, are far more effective than nonparametric methods in protecting against violation of homogeneity of variance.

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28

Wang, Wenhao, and Neal Kingston. "Using Bayesian Nonparametric Item Response Function Estimation to Check Parametric Model Fit." Applied Psychological Measurement 44, no. 5 (March 10, 2020): 331–45. http://dx.doi.org/10.1177/0146621620909906.

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Previous studies indicated that the assumption of logistic form of parametric item response functions (IRFs) is violated often enough to be worth checking. Using nonparametric item response theory (IRT) estimation methods with the posterior predictive model checking method can obtain significance probabilities of fit statistics in a Bayesian framework by accounting for the uncertainty of the parameter estimation and can indicate the location and magnitude of misfit for an item. The purpose of this study is to check the performance of the Bayesian nonparametric method to assess the IRF fit of parametric IRT models for mixed-format tests and compare it with the existing bootstrapping nonparametric method under various conditions. The simulation study results show that the Bayesian nonparametric method can detect misfit items with higher power and lower type I error rates when the sample size is large and with lower type I error rates compared with the bootstrapping method for the conditions with nonmonotonic items. In the real-data study, several dichotomous and polytomous misfit items were identified and the location and magnitude of misfit were indicated.

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29

Lembessis, Elizabeth, and Robert Fudin. "Sinistrality and Reduced Longevity: Reply to Coren and Halpern's Replay." Perceptual and Motor Skills 78, no. 2 (April 1994): 579–82. http://dx.doi.org/10.2466/pms.1994.78.2.579.

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Coren and Halpern's response to Fudin, Renninger, Lembessis, and Hirshon's 1993 report of a nonsignificant difference in longevity between left-handed and right-handed baseball players contained errors of fact, theory, and omission. Especially disturbing is that they ignored the fact that our negative findings were found not only with parametric tests but with the same nonparametric tests that they had used. We defend our use of parametric tests given the particular data we analyzed, and we comment on the databases that we and Halpern and Coren considered and the results of studies of handedness and longevity.

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30

Su, Liangjun, and Halbert White. "TESTING STRUCTURAL CHANGE IN PARTIALLY LINEAR MODELS." Econometric Theory 26, no. 6 (March 22, 2010): 1761–806. http://dx.doi.org/10.1017/s0266466609990788.

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We consider two tests of structural change for partially linear time-series models. The first tests for structural change in the parametric component, based on the cumulative sums of gradients from a single semiparametric regression. The second tests for structural change in the parametric and nonparametric components simultaneously, based on the cumulative sums of weighted residuals from the same semiparametric regression. We derive the limiting distributions of both tests under the null hypothesis of no structural change and for sequences of local alternatives. We show that the tests are generally not asymptotically pivotal under the null but may be free of nuisance parameters asymptotically under further asymptotic stationarity conditions. Our tests thus complement the conventional instability tests for parametric models. To improve the finite-sample performance of our tests, we also propose a wild bootstrap version of our tests and justify its validity. Finally, we conduct a small set of Monte Carlo simulations to investigate the finite-sample properties of the tests.

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31

M'Hirit, O., and J. G. Postaire. "A nonparametric technique for taper function estimation." Canadian Journal of Forest Research 15, no. 5 (October 1, 1985): 862–71. http://dx.doi.org/10.1139/x85-139.

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The most common method for determining tree profile models is by fitting an analytical function to a set of sample trees. In situations where the forester has relatively little a priori knowledge about the nature of profiles, choosing the analytical form of the parametric model is a critical problem without a satisfactory solution. An alternative is to use a nonparametric approach in which the model of the profile is explicitly specified by a tabulation of diameters at discrete heights. In this paper, it is shown how the model can be determined from the sample trees by means of a classical nonparametric probability density function estimation technique. Field tests and a measure of goodness of fit are used to express how well the models match the actual stem profiles. Using the Moroccan cedar (Cedrusatlantica Manetti) tree as an example, it is shown how the new nonparametric model can be compared with any classical parametric model. The results achieved demonstrate the advantages in using a nonparametric representation of stem profiles, which, moreover, is well suited to computer calculation constraints.

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32

Yang, Yuqing, Robert A. Monserud, and Shongming Huang. "An evaluation of diagnostic tests and their roles in validating forest biometric models." Canadian Journal of Forest Research 34, no. 3 (March 1, 2004): 619–29. http://dx.doi.org/10.1139/x03-230.

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Model validation is an important part of model development. It is performed to increase the credibility and gain sufficient confidence about a model. This paper evaluated the usefulness of 10 statistical tests, five parametric and five nonparametric, in validating forest biometric models. The five parametric tests are the paired t test, the Χ2 test, the separate t test, the simultaneous F test, and the novel test. The five nonparametric tests are the Brown-Mood test, the KolmogorovSmirnov test, the modified KolmogorovSmirnov test, the sign test, and the Wilcoxon signed-rank test. Nine benchmark data sets were selected to evaluate the behavior of these tests in model validation; three were collected from Alberta and six were published elsewhere. It was shown that the usefulness of statistical tests in model validation is very limited. None of the tests seems to be generic enough to work well across a wide range of models and data. Each model passed one or more tests, but not all of them. Because of this, caution should be exercised when choosing a statistical test or several tests together to try to validate a model. It is important to reduce and remove any potential personal bias in selecting a favorite test, which can influence the outcome of the results.

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33

Zimmerman, Donald W., and Bruno D. Zumbo. "Effect of Outliers on the Relative Power of Parametric and Nonparametric Statistical Tests." Perceptual and Motor Skills 71, no. 1 (August 1990): 339–49. http://dx.doi.org/10.2466/pms.1990.71.1.339.

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It is known that parametric statistical tests, such as t and F, are more powerful than their nonparametric counterparts, such as the Wilcoxon-Mann-Whitney test or the Kruskal-Wallis test, when the assumption of a normal population distribution is satisfied. However, it has been found that, for quite a few nonnormal distributions, the Wilcoxon-Mann-Whitney test ( W) is more powerful than the Student t-test ( t) both in the asymptotic limit and for small samples. The present computer-simulation study examined the role of outliers in determining the relative power of W and t. In a series of five steps, a standard normal distribution, as well as a uniform distribution, was altered so that extreme scores occurred with increasingly higher probability. It was found that the initial power advantage of t gradually diminished in favor of W. In contrast, in a series of five steps, exponential and Cauchy distributions were truncated at less and less extreme values, so that the influence of outliers was reduced, and the initial power advantage of W gradually diminished in favor of t. For all distributions, the ordinary Student t-test performed on the ranks of measures instead of the measures was affected by addition or elimination of outliers in the same way as W and yielded the same probabilities of Type I and Type II errors as W.

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34

Lopez, O., and V. Patilea. "Nonparametric lack-of-fit tests for parametric mean-regression models with censored data." Journal of Multivariate Analysis 100, no. 1 (January 2009): 210–30. http://dx.doi.org/10.1016/j.jmva.2008.04.008.

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35

Campbell, Robert A. "A comparison of parametric and nonparametric tests for detecting queue waiting time differences." Communications in Statistics - Simulation and Computation 17, no. 2 (January 1988): 463–70. http://dx.doi.org/10.1080/03610918808812674.

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36

Zimmerman, Donald W., Richard H. Williams, and Bruno D. Zurabo. "Effect of nonindependence of sample observations on some parametric and nonparametric statistical tests." Communications in Statistics - Simulation and Computation 22, no. 3 (January 1993): 779–89. http://dx.doi.org/10.1080/03610919308813123.

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37

Zimmerman, Donald W. "Invalidation of Parametric and Nonparametric Statistical Tests by Concurrent Violation of Two Assumptions." Journal of Experimental Education 67, no. 1 (January 1998): 55–68. http://dx.doi.org/10.1080/00220979809598344.

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38

Chen, Xiaohong, Han Hong, and Matthew Shum. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models." Journal of Econometrics 141, no. 1 (November 2007): 109–40. http://dx.doi.org/10.1016/j.jeconom.2007.01.010.

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39

Arize, Augustine C., Ioannis N. Kallianiotis, Ebere Eme Kalu, John Malindretos, and Moschos Scoullis. "A Multidude of Econometric Tests: Forecasting the Dutsch Guilder." International Journal of Economics and Finance 9, no. 9 (August 10, 2017): 94. http://dx.doi.org/10.5539/ijef.v9n9p94.

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This paper studies a diversity of exchange rate models, applies both parametric and nonparametric techniques to them, and examines said models’ collective predictive performance. We shall choose the forecasting predictor with the smallest root mean square forecast error (RMSE); the empirical evidence for a better type of exchange rate model is in equation (34), although none of our evidence gives an optimal forecast. At the end, these models’ error correction versions will be fit so that plausible long-run elasticities can be imposed on each model’s fundamental variables.

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40

Vaubourdolle, M., J. P. Clavel, L. Cynober, A. Piton, and A. Galli. "Acid phosphatase and zinc in semen of subjects with no clinical evidence of prostatic disease." Clinical Chemistry 31, no. 6 (June 1, 1985): 878–80. http://dx.doi.org/10.1093/clinchem/31.6.878.

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Abstract We evaluated the distribution of values for prostatic acid phosphatase (EC 3.1.3.2) activity and zinc concentration in semen of 80 men without clinical evidence of prostatic disease. Both substances have log normal distributions. Reference intervals were defined by parametric tests after logarithmic transformation of data and by nonparametric tests. There is a correlation between zinc concentration and prostatic acid phosphatase activity in semen.

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41

Tahk, Alexander. "Nonparametric Ideal-Point Estimation and Inference." Political Analysis 26, no. 2 (March 8, 2018): 131–46. http://dx.doi.org/10.1017/pan.2017.38.

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Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

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42

Munzel, U., and B. Bandelow. "The Use of Parametric vs. Nonparametric Tests in the Statistical Evaluation of Rating Scales." Pharmacopsychiatry 31, no. 06 (November 1998): 222–24. http://dx.doi.org/10.1055/s-2007-979332.

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43

Hajian-Tilaki, Karim O., James A. Hanley, Lawrence Joseph, and Jean-Paul Collet. "A Comparison of Parametric and Nonparametric Approaches to ROC Analysis of Quantitative Diagnostic Tests." Medical Decision Making 17, no. 1 (February 1997): 94–102. http://dx.doi.org/10.1177/0272989x9701700111.

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Macdonald, Paul. "Power, Type I, and Type III Error Rates of Parametric and Nonparametric Statistical Tests." Journal of Experimental Education 67, no. 4 (January 1999): 367–79. http://dx.doi.org/10.1080/00220979909598489.

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Lei, Pui-Wa, Stephen B. Dunbar, and Michael J. Kolen. "A Comparison of Parametric and Nonparametric Approaches to Item Analysis for Multiple-Choice Tests." Educational and Psychological Measurement 64, no. 4 (August 2004): 565–87. http://dx.doi.org/10.1177/0013164403261760.

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Abaluck, Jason, and Jonathan Gruber. "Choice Inconsistencies among the Elderly: Evidence from Plan Choice in the Medicare Part D Program: Reply." American Economic Review 106, no. 12 (December 1, 2016): 3962–87. http://dx.doi.org/10.1257/aer.20151318.

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We explore the in- and out-of-sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff, and Powers (2016). We argue that their nonparametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP’s suggested specification checks, and that comprehensive goodness of fit measures perform better with our model than the expected utility model. Finally, we explore the robustness of our 2011 results to alternative normative assumptions highlighting the role of brand fixed effects and unobservable characteristics. (JEL D12, H51, I13, I18, J14)

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Lmakri, Aziz, Abdelhadi Akharif, and Amal Mellouk. "Optimal Detection of Bilinear Dependence in Short Panels of Regression Data." Revista Colombiana de Estadística 43, no. 2 (July 1, 2020): 143–71. http://dx.doi.org/10.15446/rce.v43n2.83044.

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In this paper, we propose parametric and nonparametric locally andasymptotically optimal tests for regression models with superdiagonal bilinear time series errors in short panel data (large n, small T). We establish a local asymptotic normality property– with respect to intercept μ, regression coefficient β, the scale parameter σ of the error, and the parameter b of panel superdiagonal bilinear model (which is the parameter of interest)– for a given density f1 of the error terms. Rank-based versions of optimal parametric tests are provided. This result, which allows, by Hájek’s representation theorem, the construction of locally asymptotically optimal rank-based tests for the null hypothesis b = 0 (absence of panel superdiagonal bilinear model). These tests –at specified innovation densities f1– are optimal (most stringent), but remain valid under any actual underlying density. From contiguity, we obtain the limiting distribution of our test statistics under the null and local sequences of alternatives. The asymptotic relative efficiencies, with respect to the pseudo-Gaussian parametric tests, are derived. A Monte Carlo study confirms the good performance of the proposed tests.

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Motsnyi, F. V. "Analysis of Nonparametric and Parametric Criteria for Statistical Hypotheses Testing. Chapter II. Agreement Criteria of Romanovsky, Student and Fisher." Statistics of Ukraine 84, no. 1 (March 23, 2019): 13–23. http://dx.doi.org/10.31767/su.1(84)2019.01.02.

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Any assumptions or waiting for that or another distribution of random values are statistical hypotheses. The objective knowledge about hypotheses can obtain always using the spatial statistical tests that are named agreement criteria. It’s known about 100 different agreement criteria. Nonparametric tests don’t include in calculations the parameters of the probability distribution and operates with frequency only. They don’t assume that the experimental data have a specific distribution. Nonparametric criteria are widely used in analysis of the empirical data, in the checking of the hope models, the simple and complex statistical hypotheses and take a prominent place in science and practice. Parametric tests contain the distribution parameters. They are used for the samples with the normal distribution. Parametric tests permit: 1) to check the statistical hypotheses about the normal distribution characteristics of the population obtained on the base of sample processing; 2) to except the gross errors; 3) to evaluate the difference of the mathematical average values ; 4) and to distinguish the dispersions. That is why these tests are very extensively used in mathematical statistics too. The paper continues ideas of the author’s works [1; 2] devoted to advanced based tools of the mathematical statistics. The aim of the work is to generalize the well known theoretical and experimental results about the statistical tests of the hypotheses testing. Parametric criteria (Romanovsky, Student, Fisher) are discussed carefully from the uniform point of view. The peculiarities of its using for statistical hypothesis testing are highlighted. The typical tasks are suggested and solved. All this takes an opportunity to cover the main point (essence) of the problem as a whole and evaluate its actuality directly.

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Echenique, Federico, Taisuke Imai, and Kota Saito. "Testable Implications of Models of Intertemporal Choice: Exponential Discounting and Its Generalizations." American Economic Journal: Microeconomics 12, no. 4 (November 1, 2020): 114–43. http://dx.doi.org/10.1257/mic.20180028.

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We present revealed-preference characterizations of the most common models of intertemporal choice: the model of exponentially discounted concave utility, and some of its generalizations. Our characterizations take consumption data as primitives, and provide nonparametric revealed-preference tests. We apply our tests to data from two recent experiments and find that our axiomatization delivers new insights and perspectives on datasets that had been analyzed by traditional parametric methods. (JEL C91, D11, D15)

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Stinchcombe, Maxwell B., and Halbert White. "CONSISTENT SPECIFICATION TESTING WITH NUISANCE PARAMETERS PRESENT ONLY UNDER THE ALTERNATIVE." Econometric Theory 14, no. 3 (June 1998): 295–325. http://dx.doi.org/10.1017/s0266466698143013.

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The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly extend the nuisance parameter approach. How and why the nuisance parameter approach works and how it can be extended bear closely on recent developments in artificial neural networks. Statistical content is provided by viewing specification tests with nuisance parameters as tests of hypotheses about Banach-valued random elements and applying the Banach central limit theorem and law of iterated logarithm, leading to simple procedures that can be used as a guide to when computationally more elaborate procedures may be warranted.

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