ScholarGate
Assistent

Sammenlign metoder

Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.

Kjernetetthetsestimering og fordelingstesting (KDE)×Moods medianetest×
FagfeltStatistikkStatistikk
FamilieRegression modelRegression model
Opprinnelsesår19561954
OpphavspersonRosenblatt (1956); Parzen (1962); textbook treatment by SilvermanA. M. Mood
TypeNonparametric density estimationNonparametric median comparison
Opprinnelig kildeRosenblatt, 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 ↗
Aliaskernel density estimate, KDE, Parzen window estimation, nonparametric density estimationmedian test, Brown-Mood median test, Mood Medyan Testi
Relaterte43
SammendragKernel 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.
ScholarGateDatasett
  1. v1
  2. 2 Kilder
  3. PUBLISHED
  1. v1
  2. 2 Kilder
  3. PUBLISHED

Gå til søk Download slides

ScholarGateSammenlign metoder: Kernel Density Estimation · Mood's Median Test. Hentet 2026-06-15 fra https://scholargate.app/no/compare