Relevant bibliographies by topics / Test de permutation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Test de permutation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Test de permutation"

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Anderson, Marti J. "Permutation tests for univariate or multivariate analysis of variance and regression." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 3 (March 1, 2001): 626–39. http://dx.doi.org/10.1139/f01-004.

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The most appropriate strategy to be used to create a permutation distribution for tests of individual terms in complex experimental designs is currently unclear. There are often many possibilities, including restricted permutation or permutation of some form of residuals. This paper provides a summary of recent empirical and theoretical results concerning available methods and gives recommendations for their use in univariate and multivariate applications. The focus of the paper is on complex designs in analysis of variance and multiple regression (i.e., linear models). The assumption of exchangeability required for a permutation test is assured by random allocation of treatments to units in experimental work. For observational data, exchangeability is tantamount to the assumption of independent and identically distributed errors under a null hypothesis. For partial regression, the method of permutation of residuals under a reduced model has been shown to provide the best test. For analysis of variance, one must first identify exchangeable units by considering expected mean squares. Then, one may generally produce either (i) an exact test by restricting permutations or (ii) an approximate test by permuting raw data or some form of residuals. The latter can provide a more powerful test in many situations.
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Savchuk, M., and M. Burlaka. "Encoding and classification of permutations bу special conversion with estimates of class power." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 2 (2019): 36–43. http://dx.doi.org/10.17721/1812-5409.2019/2.3.

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Scientific articles investigating properties and estimates of the number of so-called complete permutations are surveyed and analyzed. The paper introduces a special S-transform on the set of permutations and determines the permutation properties according to this transform. Classification and coding of permutations by equivalence classes according to their properties with respect to S-transformation is proposed. This classification and permutation properties, in particular, generalize known results for complete permutations regarding determining certain cryptographic properties of substitutions that affect the cryptographic transformations security. The exact values of the number of permutations in equivalence classes for certain permutation sizes are calculated and the estimates of the cardinality of classes with various properties are constructed by statistical modeling. The complete list of permutation classes with the exact values of their sizes for permutations of order n = 11 is presented. The interval estimates for the size of classes with various characteristics for permutations of order n = 11, 26, 30, 31, 32, 33, 45, 55 are obtained. Monte Carlo estimates and bounds of confidence intervals used the approximation of the binomial distribution by the normal and Poisson distributions, as well as the Python programming language package Scipy. Statistical tables have been calculated that can be used for further conclusions and estimates. The classification of permutations by their properties with respect to the introduced transform can be used in constructing high-quality cryptographic transformations and transformations with special features. The classes of complete permutations with their properties are selected as the best for rotary cryptosystems applications. The obtained results can be used, in particular, to search for permutations with certain characteristics and properties, to find the probability that the characteristic of the generated permutation belongs to a collection of given characteristics, to estimate the complexity of finding permutations with certain properties. A statistical criterion of consent, which uses the characteristics of permutations by S-transformation to test the generators of random permutations and substitutions is proposed.
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Mielke, Paul W., Kenneth J. Berry, and Charles O. Neidt. "A Permutation Test for Multivariate Matched-Pair Analyses: Comparisons with Hotelling's Multivariate Matched-Pair T2 Test." Psychological Reports 78, no. 3 (June 1996): 1003–8. http://dx.doi.org/10.2466/pr0.1996.78.3.1003.

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A permutation alternative for Hotelling's multivariate matched-pair T2 test is introduced. The permutation test allows for analyses when the number of subjects is less than or equal to the number of measurements, which is not possible with Hotelling's multivariate matched-pair T2 test. For the data analyzed the permutation test is shown to provide improved discrimination over Hotelling's multivariate matched-pair T2 test.
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Sari, Resti Mustika, Yudiantri Asdi, and Ferra Yanuar. "PERBANDINGAN KUASA WILCOXON RANK SUM TEST DAN PERMUTATION TEST DALAM BERBAGAI DISTRIBUSI TIDAK NORMAL." Jurnal Matematika UNAND 3, no. 4 (December 1, 2014): 139. http://dx.doi.org/10.25077/jmu.3.4.139-146.2014.

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Kuasa dari suatu uji statistik adalah peluang menolak hipotesis nol kalauhipotesis nol tersebut salah. Simulasi dengan menggunakan software R dilakukan untukmembandingkan kuasa Wilcoxon Rank Sum Test dan Permutation Test untuk membandingkan dua nilai tengah populasi yang dibangkitkan dari distribusi Uniform, Eksponensial, Log Normal dan Weibull. Hasil simulasi data menunjukkan bahwa pada sebaranUniform uji yang lebih baik adalah Permutation Test. Untuk sampel menengah dansampel besar pada distribusi Eksponensial, Weibull dan Log-Normal uji yang lebih baikadalah Wilcoxon Rank Sum Test. Sedangkan untuk sampel berukuran kecil yang berasaldari distribusi Eksponensial, Weibull dan Log-Normal tidak bisa ditentukan mana uji yglebih baik diantara Wilcoxon Rank Sum Test dan Permutation Test.
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Collins, Mark F. "A Permutation Test for Planar Regression." Australian Journal of Statistics 29, no. 3 (September 1987): 303–8. http://dx.doi.org/10.1111/j.1467-842x.1987.tb00747.x.

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Cade, Brian S., and Jon D. Richards. "A permutation test for quantile regression." Journal of Agricultural, Biological, and Environmental Statistics 11, no. 1 (March 2006): 106–26. http://dx.doi.org/10.1198/108571106x96835.

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Basso, Dario, and Luigi Salmaso. "A permutation test for umbrella alternatives." Statistics and Computing 21, no. 1 (August 27, 2009): 45–54. http://dx.doi.org/10.1007/s11222-009-9145-8.

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Shparlinski, I. E. "A deterministic test for permutation polynomials." Computational Complexity 2, no. 2 (June 1992): 129–32. http://dx.doi.org/10.1007/bf01202000.

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Toth, Daniell. "A Permutation Test on Complex Sample Data." Journal of Survey Statistics and Methodology 8, no. 4 (August 13, 2019): 772–91. http://dx.doi.org/10.1093/jssam/smz018.

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Abstract Permutation tests are a distribution-free way of performing hypothesis tests. These tests rely on the condition that the observed data are exchangeable among the groups being tested under the null hypothesis. This assumption is easily satisfied for data obtained from a simple random sample or a controlled study after simple adjustments to the data, but there is no general method for adjusting survey data collected using a complex sample design to allow for permutation tests. In this article, we propose a general method for performing a pseudo-permutation test that accounts for the complex sample design. The proposed method is not a true permutation test in that the new values do not come from the set of observed values in general but of an expanded set of values satisfying a random-effects model on the clustered residuals of a design-consistent estimating equation. We provide a set of conditions under which this procedure leads to consistent test results. Tests using a simulated population and an application analyzing US Bureau of Labor Statistics consumer expenditure data comparing the performance of the proposed method to permutation tests that ignore the sample design demonstrate that it is necessary to account for the design features in order to obtain reasonable p value estimates.
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Berry, Kenneth J., and Paul W. Mielke. "Analysis of Multivariate Matched-Paired Data: A Fortran 77 Program." Perceptual and Motor Skills 83, no. 3 (December 1996): 788–90. http://dx.doi.org/10.2466/pms.1996.83.3.788.

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The FORTRAN program MVMPT is described which computes Hotelling's multivariate matched-pair T2 test and three permutation tests described by Miellte, et al. in 1996: an exact multivariate matched-pair permutation test, a non-asymptotic multivariate matched-pair permutation test, and the Hotelling multivariate matched-pair T2 permutation test.
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Dissertations / Theses on the topic "Test de permutation"

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Baranzano, Rosa. "Non-parametric kernel density estimation-based permutation test: Implementation and comparisons." Thesis, Uppsala universitet, Matematisk statistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-147052.

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Eckerdal, Nils. "A permutation evaluation of the robustness of a high-dimensional test." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352914.

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The present thesis is a study of the robustness and performance of a test applicable in the high-dimensional context (𝑝>𝑛) whose components are unbiased statistics (U-statistics). This test (the U-test) has been shown to perform well under a variety of circumstances and can be adapted to any general linear hypothesis. However, the robustness of the test is largely unexplored. Here, a simulation study is performed, focusing particularly on violations of the assumptions the test is based on. For extended evaluation, the performance of the U-test is compared to its permutation counterpart. The simulations show that the U-test is robust, performing poorly only when the permutation test does so as well. It is also discussed that the U-test does not inevitably rest on the assumptions originally imposed on it.
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Albert, Mélisande. "Tests d’indépendance par bootstrap et permutation : étude asymptotique et non-asymptotique. Application en neurosciences." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4079/document.

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Premièrement, nous construisons de tels tests basés sur des approches par bootstrap ou par permutation, et étudions leurs propriétés asymptotiques dans un cadre de processus ponctuels, à travers l'étude du comportement asymptotique des lois conditionnelles des statistiques de test bootstrappée et permutée, sous l'hypothèse nulle ainsi que toute alternative. Nous les validons en pratique par simulation et les comparons à des méthodes classiques en neurosciences. Ensuite, nous nous concentrons sur les tests par permutation, connus pour contrôler non-asymptotiquement leur niveau. Les p-valeurs basées sur la notion de coïncidences avec délai, sont implémentées dans une procédure de tests multiples, appelée méthode Permutation Unitary Events, pour détecter les synchronisations entre deux neurones. Nous validons la méthode par simulation avant de l'appliquer à de vraies données. Deuxièmement, nous étudions les propriétés non-asymptotiques des tests par permutation en termes de vitesse de séparation uniforme. Nous construisons une procédure de tests agrégés, basée sur du seuillage par ondelettes dans un cadre de variables aléatoires à densité. Nous déduisons d'une inégalité fondamentale de Talagrand, une nouvelle inégalité de concentration de type Bernstein pour des sommes permutées aléatoirement qui nous permet de majorer la vitesse de séparation uniforme sur des espaces de Besov faibles et d'en déduire que cette procédure semble être optimale et adaptative au sens du minimax
On the one hand, we construct such tests based on bootstrap and permutation approaches. Their asymptotic performance are studied in a point process framework through the analysis of the asymptotic behavior of the conditional distributions of both bootstrapped and permuted test statistics, under the null hypothesis as well as under any alternative. A simulation study is performed verifying the usability of these tests in practice, and comparing them to existing classical methods in Neuroscience. We then focus on the permutation tests, well known for their non-asymptotic level properties. Their p-values, based on the delayed coincidence count, are implemented in a multiple testing procedure, called Permutation Unitary Events method, to detect the synchronization occurrences between two neurons. The practical validity of the method is verified on a simulation study before being applied on real data. On the other hand, the non-asymptotic performances of the permutation tests are studied in terms of uniform separation rates. A new aggregated procedure based on a wavelet thresholding method is developed in the density framework. Based on Talagrand's fundamental inequalities, we provide a new Bernstein-type concentration inequality for randomly permuted sums. In particular, it allows us to upper bound the uniform separation rate of the aggregated procedure over weak Besov spaces and deduce that this procedure seems to be optimal and adaptive in the minimax sens
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Yu, Li. "Tau-Path Test - A Nonparametric Test For Testing Unspecified Subpopulation Monotone Association." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1255657068.

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Kačkina, Julija. "Svertinių rodiklių agregavimo lygmens parinkimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20090908_201759-89606.

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Šiame darbe aš apibendrinau informaciją apie pasirinkimo tarp tiesinio prognozavimo mikro ir makro-modelių problemą. Agregavimas suprantamas kaip sektorinis agregavomas, o modeliai yra iš vienmatės tiesinės regresijos klasės. Aš išvedžiau kriterijų pasirinkimui tarp makro ir mikro-modelių ir idealaus agregavimo testą tiesinio agregavimo su fiksuotais ir atsitiktiniais svoriais atvejais. Paskutiniu atveju idealų agregavimą rekomenduoju tikrinti permutaciniu testu. Rezultatai iliustruoju ekonominiu pavyzdžiu. Modeliuoju Lietuvos vidutinį darbo užmokestį agreguotu modeliu ir atskirose ekonominės veiklos sektoriuose. Analizės rezultatas parodo, kad modeliai yra ekvivalentūs.
This paper focuses on the choice between macro and micro models. I suggest a hypothesis testing procedure for in-sample model selection for such variables as average wage. Empirical results show that Lithuanian average wage should be predict by using aggregate model.
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Zhang, Yan. "The impact of midbrain cauterize size on auditory and visual responses' distribution." unrestricted, 2009. http://etd.gsu.edu/theses/available/etd-04202009-145923/.

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Thesis (M.S.)--Georgia State University, 2009.
Title from file title page. Yu-Sheng Hsu, committee chair; Xu Zhang, Sarah. L. Pallas, committee members. Description based on contents viewed June 12, 2009. Includes bibliographical references (p. 37). Appendix A: SAS code: p. 38-53.
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Shadrokh, Ali. "Analyse comparative des tests de permutations en régression multiple et application à l'analyse de tableaux de distances." Université Joseph Fourier (Grenoble), 2006. http://www.theses.fr/2006GRE10084.

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Lorsque le processus de génération des données ne respecte pas certains des postulats fondant l'analyse statistique du modèle classique de régression linéaire, les tests de permutations offrent une alternative non paramétrique fiable de construction de tests d'hypothèse libres. La première application de cette méthode d'inférence statistique au modèle de régression linéaire simple renvoie à Fisher (1935) et Pitman (1937a,b,1938). Cette méthode de ré-échantillonnage est fondée sur des postulats moins forts que la méthode paramétrique classique et facilement vérifiables en pratique: l'échangeabilité des observations sous l'hypothèse nulle. Si l'utilisation des tests de permutation fait consensus en régression linéaire simple et pour tester l'adéquation d'un modèle en régression multiple, le problème se complique lorsqu'on souhaite mettre à l'épreuve une hypothèse de nullité d'un coefficient de régression partielle. L'étude des conditions d'échangeabilité n'est plus simple dans ce cas. Il n'est alors plus possible de construire des tests exactsplusieurs propositions de tests sont en concurrence. L'objectif principal de notre travail est la comparaison des tests de permutation adaptés aux hypothèses de nullité d'un coefficient de régression partielle dans un modèle linéaire à p variables explicatives, conditionnellement à l'observation d'un échantillon. Quatre méthodes sont comparées, d'une part en recourant à des simulations effectuées dans le cas d'une régression double, puis théoriquement, afin de déterminer les propriétés de biais, de couverture et de puissance de ces tests. Les résultats obtenus sont ensuite étendus au cas de la régression linéaire multiple. Un dernier chapitre complète cette étude en traitant le problème de test de la dépendance partielle entre tableaux de distances interpoints. Nous avons comparé les adaptations des quatre méthodes de test de permutation à ce contexte marqué par la dépendance existant entre éléments d'une matrice de distance et nous avons obtenu dans ce cas des résultats tout à fait différents de ceux qui caractérisent la situation classique d'une régression linéaire sur échantillon indépendant, id est dans le cas précédent
When the data generation process does not satisfy some of the assumptions founding the statistical inferences in the classic linear regression model, permutation tests offer a reliable nonparametric alternative for constructing distribution-free tests. The first application of the permutation test method%gy for statistical inference on the simple linear regression model can be traced back to papers by Fisher (1935) and Pitman (1937a, b, 1938). This resampling method is founded on hypothesis weaker than the ciassic parametric approach and which are easily checkable in practice: the exchangeability of the observations under the null hypothesis. There is general agreement concerning an appropriate permutation method yielding exact tests of hypotheses in the simple linear regression mode!. This is not the case, however, for partial tests needed in multiple linear regressions. Then, the problem becomes much trickier to test a null hypothesis concerning one partial regression coefficient. Due exchangeability properties are no more satisfied, and thus no exact test exists for that problem. Several asymptotically exact candidate methods have been proposed in that case. The main goal of our work aims at comparison of permutation test startegies adapted to the hypotheses of nullity of a partial coefficient regression in a linear regression model with p explanatory variables, conditionally on the information contained in the sam pie at hand. Four permutation test methods are compared, first on simulated data resorting to the double linear regression model, and then on theoretical grounds, in order to explore their unbiasedness properties, as weil as their power function's hierarchy. The results obtained are then extended to the general multiple linear regressions setting. A final chapter supplements our research by focussing on inferential problems met when dealing with partial dependence structures between inter-point distance matrices of finite order. We compared the adaptation of four candidate permutation test strategies in this context, the specificity of which relies on the complexities induced by the dependence structure existing between e/ements of a distance matrix. Therefore, we obtained resu/ts that revealed themselves quite different in this case from those obtained in the classic situation of linear regression applied to independent sam pies, which is the object of our simulations and formal developments presented in the first part of the thesis
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ZHONG, WEI. "STATISTICAL APPROACHES TO ANALYZE CENSORED DATA WITH MULTIPLE DETECTION LIMITS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1130204124.

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Annica, Ivert. "Determining Attribute Importance Using an Ensemble of Genetic Programs and Permutation Tests : Relevansbestämning av attribut med hjälp av genetiska program och permutationstester." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185260.

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When classifying high-dimensional data, a lot can be gained, in terms of both computational time and precision, by only considering the most important features. Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. Often, this assumption can help eliminate redundancies and produce good predictors using only a small subset of features. However, when the predictability depends on interactions between the features, such methods will fail to produce satisfactory results. Also, since the suitability of the selected features depends on the learning algorithm in which they will be used, correlation-based filter methods might not be optimal when using genetic programs as the final classifiers, as they fail to capture the possibly complex relationships that are expressible by the genetic programming rules. In this thesis a method that can find important features, both independently and dependently discriminative, is introduced. This method works by performing two different types of permutation tests that classifies each of the features as either irrelevant, independently predictive or dependently predictive. The proposed method directly evaluates the suitability of the features with respect to the learning algorithm in question. Also, in contrast to computationally expensive wrapper methods that require several subsets of features to be evaluated, a feature classification can be obtained after only one single pass, even though the time required does equal the training time of the classifier. The evaluation shows that the attributes chosen by the permutation tests always yield a classifier at least as good as the one obtained when all attributes are used during training - and often better. The proposed method also fares well when compared to other attribute selection methods such as RELIEFF and CFS.
Då man handskas med data av hög dimensionalitet kan man uppnå både bättre precision och förkortad exekveringstid genom att enbart fokusera på de viktigaste attributen. Många metoder för att hitta viktiga attribut är baserade på ett grundantagande om en stark korrelation mellan de viktiga attributen och dess tillhörande klass, men ofta även på ett oberoende mellan de individuella attributen. Detta kan å ena sidan leda till att överflödiga attribut lätt kan elimineras och därmed underlätta processen att hitta en bra klassifierare, men å andra sidan också ge missvisande resultat ifall förmågan att separera klasser i hög grad beror på interaktioner mellan olika attribut. Då lämpligheten av de valda attributen också beror på inlärningsalgoritmen i fråga är det troligtvis inte optimalt att använda sig av metoder som är baserade på korrelationer mellan individuella attribut och dess tillhörande klass, ifall målet är att skapa klassifierare i form av genetiska program, då sådana metoder troligtvis inte har förmågan att fånga de komplexa interaktioner som genetiska program faktiskt möjliggör. Det här arbetet introducerar en metod för att hitta viktiga attribut - både de som kan klassifiera data relativt oberoende och de som får sina krafter endast genom att utnyttja beroenden av andra attribut. Den föreslagna metoden baserar sig på två olika typer av permutationstester, där attribut permuteras mellan de olika dataexemplaren för att sedan klassifieras som antingen oberende, beroende eller irrelevanta. Lämpligheten av ett attribut utvärderas direkt med hänsyn till den valda inlärningsalgoritmen till skillnad från så kallade wrappers, som är tidskrävande då de kräver att flera delmängder av attribut utvärderas. Resultaten visar att de attribut som ansetts viktiga efter permutationstesten genererar klassifierare som är åtminstone lika bra som när alla attribut används, men ofta bättre. Metoden står sig också bra när den jämförs med andra metoder som till exempel RELIEFF och CFS.
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Fu, Min. "A RESAMPLING BASED APPROACH IN EVALUATION OF DOSE-RESPONSE MODELS." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/300992.

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Statistics
Ph.D.
In this dissertation, we propose a computational approach using a resampling based permutation test as an alternative to MCP-Mod (a hybrid framework integrating the multiple comparison procedure and the modeling technique) and gMCP-Mod (generalized MCP-Mod) [11], [29] in the step of identifying significant dose-response signals via model selection. We name our proposed approach RMCP-Mod or gRMCP-Mod correspondingly. The RMCP-Mod/gRMCP-Mod transforms the drug dose comparisons into a dose-response model selection issue via multiple hypotheses testing, an area where not much extended researches have been done, and solve it using resampling based multiple testing procedures [38]. The proposed approach avoids the inclusion of the prior dose-response knowledge known as "guesstimates" used in the model selection step of the MCP-Mod/gMCP-Mod framework, and therefore reduces the uncertainty in the significant model identification. When a new drug is being developed to treat patients with a specified disease, one of the key steps is to discover an optimal drug dose or doses that would produce the desired clinical effect with an acceptable level of toxicity. In order to nd such a dose or doses (different doses may be able to produce the same or better clinical effect with similar acceptable toxicity), the underlying dose-response signals need to be identified and thoroughly examined through statistical analyses. A dose-response signal refers to the fact that a drug has different clinical effects at many quantitative dose levels. Statistically speaking, the dose-response signal is a numeric relationship curve (shape) between drug doses and the clinical effects in quantitative measures. It's often been a challenge to nd correct and accurate efficacy and/or safety dose-response signals that would best describe the dose-effect relationship in the drug development process via conventional statistical methods because the conventional methods tend to either focus on a fixed, small number of quantitative dosages or evaluate multiple pre-denied dose-response models without Type I error control. In searching for more efficient methods, a framework of combining both multiple comparisons procedure (MCP) and model-based (Mod) techniques acronymed MCP-Mod was developed by F. Bretz, J. C. Pinheiro, and M. Branson [11] to handle normally distributed, homoscedastic dose response observations. Subsequently, a generalized version of the MCP- Mod named gMCP-Mod which can additionally deal with binary, counts, or time-to-event dose-response data as well as repeated measurements over time was developed by J. C. Pinheiro, B. Bornkamp, E. Glimm and F. Bretz [29]. The MCP-Mod/gMCP-Mod uses the guesstimates" in the MCP step to pre-specify parameters of the candidate models; however, in situations where the prior knowledge of the dose-response information is difficult to obtain, the uncertainties could be introduced into the model selection process, impacting on the correctness of the model identification. Throughout the evaluation of its application to the hypothetical and real study examples as well as simulation comparisons to the MCP-Mod/gMCP-Mod, our proposed approach, RMCP-Mod/gRMCP-Mod seems a viable method that can be used in the practice with some further improvements and researches that are still needed in applications to broader dose-response data types.
Temple University--Theses
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Books on the topic "Test de permutation"

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Czaplewski, Raymond L. Expected value and variance of Moran's bivariate spatial autocorrelation statistic for a permutation test. Fort Collins, Colo. (240 W. Prospect Rd., Fort Collins 80526): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1993.

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Bradbury, Ian Stuart. Permutation tests. Birmingham: University of Birmingham, 1987.

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Good, Phillip. Permutation Tests. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3235-1.

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Good, Phillip. Permutation Tests. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-2346-5.

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SALMASO, LUIGI, and Chiara Brombin. Permutation Tests in Shape Analysis. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8163-8.

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Permutation, parametric and bootstrap tests of hypotheses. 3rd ed. New York: Springer, 2005.

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Solari, Aldo, Luigi Salmaso, Fortunato Pesarin, and Dario Basso. Permutation Tests for Stochastic Ordering and ANOVA. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-85956-9.

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Pesarin, Fortunato. Permutation tests for complex data: Theory, applications, and software. Hoboken, N.J: Wiley, 2010.

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Pesarin, Fortunato. Permutation tests for complex data: Theory, applications, and software. Hoboken, N.J: Wiley, 2010.

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Good, Phillip I. Permutation tests: A practical guide to resampling methods for testing hypotheses. 3rd ed. New York: Springer, 2005.

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Book chapters on the topic "Test de permutation"

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Stanberry, Larissa. "Permutation Test." In Encyclopedia of Systems Biology, 1678. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1186.

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Basso, Dario, Fortunato Pesarin, and Luigi Salmaso. "A permutation test for umbrella alternatives." In Contributions to Statistics, 193–208. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2385-1_12.

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Chung, Moo K., Linhui Xie, Shih-Gu Huang, Yixian Wang, Jingwen Yan, and Li Shen. "Rapid Acceleration of the Permutation Test via Transpositions." In Connectomics in NeuroImaging, 42–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_5.

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Radivojac, Predrag, Zoran Obradovic, A. Keith Dunker, and Slobodan Vucetic. "Feature Selection Filters Based on the Permutation Test." In Machine Learning: ECML 2004, 334–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_32.

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Bonnini, Stefano, Eleonora Carrozzo, and Luigi Salmaso. "A Review on Heterogeneity Test: Some Permutation Procedures." In Theoretical and Applied Statistics, 13–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05420-5_2.

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Leimer, I., H. Peil, and J. Ellenberger. "Statistical Analysis of the Micronucleus Test with the Fisher-Pitman Permutation Test." In Statistical Methods in Toxicology, 20–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-48736-1_3.

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Bavaud, François. "Testing Spatial Autocorrelation in Weighted Networks: The Modes Permutation Test." In Advances in Spatial Science, 67–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30196-9_4.

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Qu, Wen, Haiyan Liu, and Zhiyong Zhang. "Permutation Test of Regression Coefficients in Social Network Data Analysis." In Springer Proceedings in Mathematics & Statistics, 377–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43469-4_28.

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Ng, Bernard, Jean Baptiste Poline, Bertrand Thirion, and Michael Greicius. "Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations." In Lecture Notes in Computer Science, 113–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19992-4_9.

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Luo, Chuanjiang, Guoyin Wang, and Feng Hu. "Two-Step Gene Feature Selection Algorithm Based on Permutation Test." In Rough Sets and Current Trends in Computing, 249–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32115-3_30.

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Conference papers on the topic "Test de permutation"

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Mao, Yunlong, and Yuan Zhang. "Privacy Preserving Distributed Permutation Test." In ASIA CCS '16: ACM Asia Conference on Computer and Communications Security. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2898445.2898450.

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Sanz-Gonzalez, J. L., F. Alvarez-Vaquero, and J. E. Gonzalez-Garcia. "Permutation test algorithms for nonparametric radar detection." In IET International Conference on Radar Systems 2007. IEE, 2007. http://dx.doi.org/10.1049/cp:20070542.

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Stratis, Panagiotis, and Ajitha Rajan. "Test case permutation to improve execution time." In ASE'16: ACM/IEEE International Conference on Automated Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2970276.2970331.

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Datta, Kamalika, Indranil Sengupta, Hafizur Rahaman, and Rolf Drechsler. "An evolutionary approach to reversible logic synthesis using output permutation." In 2013 Design and Test Symposium (IDT). IEEE, 2013. http://dx.doi.org/10.1109/idt.2013.6727117.

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Alvarez-Vaquero, Francisco, and Jose L. Sanz-Gonzalez. "Complexity analysis of permutation test versus rank test for nonparametric radar detection." In Optical Science, Engineering and Instrumentation '97, edited by William J. Miceli. SPIE, 1997. http://dx.doi.org/10.1117/12.279467.

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Alvarez-Vaquero, Francisco, and Jose L. Sanz-Gonzalez. "Signal processing algorithms by permutation test in radar application." In SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Franklin T. Luk. SPIE, 1996. http://dx.doi.org/10.1117/12.255427.

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Jaamoum, Amine, Thomas Hiscock, and Giorgio Di Natale. "Scramble Cache: An Efficient Cache Architecture for Randomized Set Permutation." In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021. http://dx.doi.org/10.23919/date51398.2021.9473919.

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Grzegorzewski, Przemyslaw. "Permutation k-sample Goodness-of-Fit Test for Fuzzy Data." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177765.

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Grishchenko, M. V., A. A. Yakimenko, and M. S. Khairetdinov. "Permutation test implementation for testing set of genetic hypotheses using GPU." In 2016 11th International Forum on Strategic Technology (IFOST). IEEE, 2016. http://dx.doi.org/10.1109/ifost.2016.7884158.

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Huang, Gan, and Zhiguo Zhang. "Improving sensitivity of cluster-based permutation test for EEG/MEG data." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2017. http://dx.doi.org/10.1109/ner.2017.8008279.

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Reports on the topic "Test de permutation"

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Bugni, Federico A., and Joel L. Horowitz. Permutation tests for equality of distributions of functional data. The IFS, March 2018. http://dx.doi.org/10.1920/wp.cem.2018.1818.

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Kamat, Vishal, and Ivan A. Canay. Approximate permutation tests and induced order statistics in the regression discontinuity design. Institute for Fiscal Studies, June 2015. http://dx.doi.org/10.1920/wp.cem.2015.2715.

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Canay, Ivan A., and Vishal Kamat. Approximate permutation tests and induced order statistics in the regression discontinuity design. IFS, August 2016. http://dx.doi.org/10.1920/wp.cem.2016.3316.

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Canay, Ivan A., and Vishal Kamat. Approximate permutation tests and induced order statistics in the regression discontinuity design. The IFS, May 2017. http://dx.doi.org/10.1920/wp.cem.2017.2117.

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Permutation tests for equality of distributions of functional data. Cemmap, March 2021. http://dx.doi.org/10.47004/wp.cem.2021.0621.

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