Koichi Inada

A minimax regret estimator of a normal mean after preliminary test

SummaryThis paper considers the problem of estimating a normal mean from the point of view of the estimation after preliminary test of significance. But our point of view is different from the usual one. The difference is interpretation about a null hypothesis. Let % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqFj0xd9q8as0-LqLs-Jirpepeea0-as0Fb9pgea0db9fr-xfr-x% frpeWZqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiqadIfagaqeaa% aa!3A2E!

On double stage minimum discrimination information estimators of the interval constrained normal mean

\bar X

Measures for the structure of clustering and admissibilities of its algorithm

denote the sample mean based on a sample of sizen from a normal population with unknown mean μ and known varianceσ2. We consider the estimator that assumes the value % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqFj0xd9q8as0-LqLs-Jirpepeea0-as0Fb9pgea0db9fr-xfr-x% frpeWZqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiabeM8a3jqadI% fagaqeaaaa!3BFB!

Crisp and fuzzy k-means clustering algorithms for multivariate functional data

\omega \bar X

8. Functional Data Analysis

when % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqFj0xd9q8as0-LqLs-Jirpepeea0-as0Fb9pgea0db9fr-xfr-x% frpeWZqaaeaabiGaciaacaqabeaadaqaaqaaaOqaamaaemaabaGabm% iwayaaraaacaGLhWUaayjcSdWaaSGbaeaacqGH8aapcaWGdbGaeq4W% dmhabaWaaOaaaeaatCvAUfKttLearyqr1ngBPrgaiuGacqWFUbGBaS% qabaaaaaaa!4759!