Bayesian Sample Size Determination with Lowest Cost by Using Numerical Methods
نویسندگان
چکیده مقاله:
In this article, the method of determining the optimal sample size is based on Linex asymmetric loss function and has been expressed through Bayesian method for normal, Poisson and exponential distributions. The desirable sample size has been calculated through numerical method. In numerical method, the average posterior risk is calculated and then it is added to the Lindley linear cost function to achieve the average of the total cost. Then, the diagram of sample size is drawn in comparison to the average of total cost and eventually, the optimal sample size that minimizes the cost has been achieved.
منابع مشابه
Bayesian sample size Determination Using a Scaled Exponential Utility Function According to Numerical Method
In this paper we propose a utility function and obtain the Bayese stimate and the optimum sample size under this utility function. This utility function is designed especially to obtain the Bayes estimate when the posterior follows a gamma distribution. We consider a Normal with known mean, a Pareto, an Exponential and a Poisson distribution for an optimum sample size under the propose...
متن کاملBayesian Determination of Sample Size in Longitudinal Studies with Binary Responses Using Random Effects Models
Sample size determination is important in all statistical studies including longitudinal studies. This is usually done by considering a target power to reduce the costs of sampling. Choosing the right sample size using efficient methods, ensures that the researcher achieve goal of the study, by spending the least amount of energy, time and money. In this article, using a method based on simulat...
متن کاملBayesian Sample Size Computing for Estimation of Binomial Proportions using p-tolerance with the Lowest Posterior Loss
This paper is devoted to computing the sample size of binomial distribution with Bayesian approach. The quadratic loss function is considered and three criterions are applied to obtain p-tolerance regions with the lowest posterior loss. These criterions are: average length, average coverage and worst outcome.
متن کاملBayesian Sample size Determination for Longitudinal Studies with Continuous Response using Marginal Models
Introduction Longitudinal study designs are common in a lot of scientific researches, especially in medical, social and economic sciences. The reason is that longitudinal studies allow researchers to measure changes of each individual over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. A st...
متن کاملEstimation of Bayesian Sample Size for Binomial Proportions Using Areas P-tolerance with Lowest Posterior Loss
STIMATION of sample size is one of the questions that we often encounter in research and applied designs. Various methods have been proposed to estimate Bayesian sample size. Freedman and Spiegelhalter [11] and Spiegelhalte et al. [12], used Bayesian approach for anticipating hypothesis testing power. Adcock [1], Pham Gia and Turkkan [10] used it for interval estimation based on normal approxim...
متن کاملUsing historical data for Bayesian sample size determination
We consider the sample size determination (SSD) problem, which is a basic yet extremely important aspect of experimental design. Specifically, we deal with the Bayesian approach to SSD, which gives researchers the possibility of taking into account pre-experimental information and uncertainty on unknown parameters. At the design stage, this fact offers the advantage of removing or mitigating ty...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 21 شماره 2
صفحات 25- 31
تاریخ انتشار 2017-03
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
کلمات کلیدی برای این مقاله ارائه نشده است
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023