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कर्नेल घनत्व आकलन एवं वितरण परीक्षण (KDE)×मूड का माध्यिका परीक्षण×
क्षेत्रसांख्यिकीसांख्यिकी
परिवारRegression modelRegression model
उद्भव वर्ष19561954
प्रवर्तकRosenblatt (1956); Parzen (1962); textbook treatment by SilvermanA. M. Mood
प्रकारNonparametric density estimationNonparametric median comparison
मौलिक स्रोतRosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗Mood, A. M. (1954). On the Asymptotic Efficiency of Certain Nonparametric Two-Sample Tests. Annals of Mathematical Statistics, 25(3), 514-522. DOI ↗
उपनामkernel density estimate, KDE, Parzen window estimation, nonparametric density estimationmedian test, Brown-Mood median test, Mood Medyan Testi
संबंधित43
सारांशKernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.Mood's median test is a nonparametric procedure that compares the medians of k independent groups by counting how many observations in each group fall above and below the pooled (grand) median, then applying a chi-square test to the resulting 2×k contingency table. It traces to A. M. Mood's 1954 work on nonparametric two-sample tests.
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  1. v1
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  3. PUBLISHED

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