Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Yoshihiko Konno"'
Publikováno v:
Symmetry, Vol 14, Iss 2, p 186 (2022)
Traditional bivariate meta-analyses adopt the bivariate normal model. As the bivariate normal distribution produces symmetric dependence, it is not flexible enough to describe the true dependence structure of real meta-analyses. As an alternative to
Externí odkaz:
https://doaj.org/article/b50a33c17fb345b991c692a188327d10
Publikováno v:
Axioms, Vol 10, Iss 4, p 267 (2021)
Meta-analyses combine the estimators of individual means to estimate the common mean of a population. However, the common mean could be undefined or uninformative in some scenarios where individual means are “ordered” or “sparse”. Hence, asse
Externí odkaz:
https://doaj.org/article/ee92e53a66084b65bee695ae43fb0f00
Publikováno v:
Statistics. 53:673-695
We propose a bivariate Farlie–Gumbel–Morgenstern (FGM) copula model for bivariate meta-analysis, and develop a maximum likelihood estimator for the common mean vector. With the aid of novel mathema...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e664e178cdbef0597157bbacbd2bff67
https://doi.org/10.1080/02331888.2019.1581782
https://doi.org/10.1080/02331888.2019.1581782
In this paper, we propose a class of general pretest estimators for the univariate normal mean. The main mathematical idea of the proposed class is the adaptation of randomized tests, where the randomization probability is related to a shrinkage para
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b69645bb5a9a3017585334d767697e5
Autor:
Takeshi Emura, Yoshihiko Konno
Publikováno v:
Statistical Papers. 55:1233-1236
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4da16ea2090098bf1ee7fea801d90976
https://doi.org/10.1007/s00362-014-0626-2
https://doi.org/10.1007/s00362-014-0626-2
Publikováno v:
Lifetime Data Analysis. 21:397-418
Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::643a48f8560cddc95976140d48ba13e9
https://doi.org/10.1007/s10985-014-9297-5
https://doi.org/10.1007/s10985-014-9297-5
Autor:
Yoshihiko, Konno
Publikováno v:
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), 1992 Jun 01. 54(2), 241-251.
Externí odkaz:
https://www.jstor.org/stable/25050877
Autor:
Takeshi Emura, Yoshihiko Konno
Publikováno v:
Computational Statistics & Data Analysis. 56:2237-2250
Suppose that one can observe bivariate random variables (L,X) only when L@?X holds. Such data are called left-truncated data and found in many fields, such as experimental education and epidemiology. Recently, a method of fitting a parametric model o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::249a92037919594c089d75da10cca357
https://doi.org/10.1016/j.csda.2011.12.022
https://doi.org/10.1016/j.csda.2011.12.022
Autor:
Yoshihiko Konno
Publikováno v:
Communications in Statistics - Theory and Methods. 39:1490-1497
The problem of estimating multivariate complex normal covariance matrices is considered under an invariant quadratic loss function. Estimators which dominate the best scalar multiple of the empirical covariance matrix are presented. Improved estimato
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::052a9b0e112790b319a5ba3661a2b7aa
https://doi.org/10.1080/03610920802265194
https://doi.org/10.1080/03610920802265194
Autor:
Takeshi Emura, Yoshihiko Konno
Publikováno v:
Statistical Papers. 53:133-149
Many statistical methods for truncated data rely on the independence assumption regarding the truncation variable. In many application studies, however, the dependence between a variable X of interest and its truncation variable L plays a fundamental
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0552ee93500343bfeed218f67c5071b0
https://doi.org/10.1007/s00362-010-0321-x
https://doi.org/10.1007/s00362-010-0321-x