When data are found to be realizations of a specific distribution, constructing the probability density function based on this distribution may not lead to the best prediction result. In this study, numerical simulations are conducted using data that follow a normal distribution,...
Open access
Research Article10.9734/jamcs/2021/v36i1030410
In this paper, AIC (Akaike's Information Criterion) is used to judge whether a coin is biased or not using the sequence of heads and tails produced by tossing the coin several times. It is well known that AIC·(−0:5) is an efficient estimator of the expected log-likelihood when th...
Open access
Research Article10.9734/jamcs/2019/v34i230212
This paper proposes a method for constructing a predictive estimator for logistic regression. We make a provisional assumption that the predictive estimator is given by multiplying the maximum likelihood estimators by constants, which are estimated using a parametric bootstrap me...
Open access
Research Article10.9734/jamcs/2019/v32i530154
Various methods are available for choosing statistical models. It is difficult to know which model selection criterion is the best for specific data. This paper discusses a method for choosing the model selection criterion based on the characteristics of the data and models. As a...
Open access
Research Article10.9734/JAMCS/2018/43344
The predictive estimator of the gradient in simple regression is assumed to be the product of the gradient given by least-squares fitting and a constant (ρ). The results of numerical simulations show that when generalized cross-validation is used to obtain the optimal ρ, the resu...
Open access
Research Article10.9734/JAMCS/2017/35869
An optimal estimator in the light of future data (i.e., a predictive estimator) is obtained using numerical simulations. The predictive estimator is assumed to be one of various functions of the maximum likelihood estimator. We then formulate an estimator that yields better resul...
Open access
Research Article10.9734/BJMCS/2016/29941
The maximum likelihood method in view of future data (i.e., the maximization of expected loglikelihood) enables estimates of geometric distribution parameter. This estimator is de ned as an estimator in which n (number of data) in the maximum likelihood estimator is replaced with...
Open access
Research Article10.9734/BJMCS/2015/20522
A predictive estimator for estimating the parameter of binomial distribution is suggested. This estimator aims to maximize the expectation of expected log-likelihood. The results given by this estimator are superior to those given by the maximum likelihood estimator in terms of t...
Open access
Research Article10.9734/BJMCS/2015/19191
In this study, the applicability of a multiple regression equation to predict breeding values based on the high-density SNP (single nucleotide polymorphism) markers that are found in the whole genome sequences of animals and plants was evaluated. The genotypes of a large number o...
Open access
Research Article10.9734/BJMCS/2015/16480
The maximum likelihood and restricted (or residual) likelihood methods are common tools for estimating variances in linear mixed models. However, regression in the light of future data can yield different results. Investigations into the characteristics of this new variance are e...
Open access
Research Article10.9734/BJMCS/2015/15514
The maximum likelihood estimator does not always maximize the expected log-likelihood when estimating the exponential distribution using a small data set. The maximum likelihood estimator should be multiplied by a positive constant that only depends on the amount of data.
Open access
Research Article10.9734/BJMCS/2015/13658