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Your search keyword '"Rahayu, Anita"' showing total 2 results
Start Over Author "Rahayu, Anita" Topic maximum likelihood statistics Topic parameter estimation Publication Year Range Last 50 years
2 results on '"Rahayu, Anita"'

1. The importance of derivative validation in data science process.

Author

Rahayu, Anita, Pangestu, Gusti, Yulianto, Y., and Wicaksono, Danang Wahyu

Subjects
*SCIENTIFIC computing, *MAXIMUM likelihood statistics, *DATA science, *PARAMETER estimation, *DATA modeling
Abstract

Data science is a science that is currently very popular and can be applied in various industries. Of course, the need for proficient people in this field is growing in demand by many businesses, including large corporations. Data science is a science that combines expertise in a particular field of science with programming, mathematics, and statistics skills. The goal is to extract knowledge or information from data. The problem that usually occurs in the data science process is at the data modeling stage, namely the accuracy of the mathematical model that will be used to obtain the estimated parameter values. To overcome this problem, this research was conducted to test the correctness of analytic derivatives in mathematical models and conduct data modeling using analytic derivatives that have been tested to obtain the estimated value of each parameter. The validation process is carried out using an analytic and numeric gradient comparison. If the comparison of the two values is close to zero, then the analytical derivatives used are correct. The parameter estimation method used is the Maximum Likelihood Estimation. In this research, it was concluded that the value of the analytic gradient is the same or close to the numeric gradient value for each parameter so that the analytic derivatives are correct and can be used for the next stage. With the results of this research, the expected implication is to get more accurate data modeling so that the conclusions obtained are more precise. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Multivariate Gamma Regression: Parameter Estimation, Hypothesis Testing, and Its Application.

Author

Rahayu, Anita, Purhadi, Sutikno, and Prastyo, Dedy Dwi

Subjects
*GAMMA distributions, *PARAMETER estimation, *LIKELIHOOD ratio tests, *MAXIMUM likelihood statistics, *DISTRIBUTION (Probability theory), *DATA distribution
Abstract

Gamma distribution is a general type of statistical distribution that can be applied in various fields, mainly when the distribution of data is not symmetrical. When predictor variables also affect positive outcome, then gamma regression plays a role. In many cases, the predictor variables give effect to several responses simultaneously. In this article, we develop a multivariate gamma regression (MGR), which is one type of non-linear regression with response variables that follow a multivariate gamma (MG) distribution. This work also provides the parameter estimation procedure, test statistics, and hypothesis testing for the significance of the parameter, partially and simultaneously. The parameter estimators are obtained using the maximum likelihood estimation (MLE) that is optimized by numerical iteration using the Berndt–Hall–Hall–Hausman (BHHH) algorithm. The simultaneous test for the model's significance is derived using the maximum likelihood ratio test (MLRT), whereas the partial test uses the Wald test. The proposed MGR model is applied to model the three dimensions of the human development index (HDI) with five predictor variables. The unit of observation is regency/municipality in Java, Indonesia, in 2018. The empirical results show that modeling using multiple predictors makes more sense compared to the model when it only employs a single predictor. [ABSTRACT FROM AUTHOR]

Published
2020
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