Results 21 to 30 of about 5,140,391 (304)

Uncertainty under a multivariate nested-error regression model with logarithmic transformation [PDF]

, 2006
Assuming a multivariate linear regression model with one random factor, we consider the parameters defined as exponentials of mixed effects, i.e., linear combinations of fixed and random effects.
Molina, Isabel
core   +1 more source

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

JOIV: International Journal on Informatics Visualization, 2023
A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept.
Salam Abdulkhaleq Noaman, Ali Mohammed Saleh Ahmed, Aseel Dawod Salman   +2 more
doaj   +1 more source

A weighted error-minimizer parameter estimation technique for one-inflated positive Poisson distribution

Results in Control and Optimization
An error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root ...
Razik Ridzuan Mohd Tajuddin
doaj   +1 more source

Estimating a Bounded Normal Mean Relative to Squared Error Loss Function [PDF]

Journal of Sciences, Islamic Republic of Iran, 2011
Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible ...
A. Karimnezhad
doaj  

Mean absolute error (MAE), R squared, root mean squared error (RMSE), symmetric mean absolute percentage error (SMAPE) for train, test & validation data.

, 2023
Mean absolute error (MAE), R squared, root mean squared error (RMSE), symmetric mean absolute percentage error (SMAPE) for train, test & validation data.
Swapnashree Satapathy (15426889), Ruchi Bhuyan (15426883), Dattatreya Kar (2817637), Abdul Akbar (15426880), Bibhudutta Pattnaik (15426892), Sanat Kumar Bhuyan (15426877), Guru Charan Nayak (15426886), Akankshya Sahu (15426874), Ananya Kuanar (3392471), Gayatree Nayak (15426871)   +9 more
core   +1 more source

Mean squared errors of small area estimators under a unit-level multivariate model [PDF]

, 2005
This work deals with estimating the vector of means of characteristics of small areas. In this context, a unit level multivariate model with correlated sampling errors is considered.
Amparo Baíllo, Baíllo, Amparo, Molina, Isabel, Isabel Molina   +3 more
core   +1 more source

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Energies
Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous ...
Burhan U Din Abdullah, Shahbaz Ahmad Khanday, Nair Ul Islam, Suman Lata, Hoor Fatima, Sarvar Hussain Nengroo   +5 more
doaj   +1 more source

Optimum design of chamfer masks using symmetric mean absolute percentage error

EURASIP Journal on Image and Video Processing, 2019
Distance transform, a central operation in image and video analysis, involves finding the shortest path between feature and non-feature entries of a binary image.
Baraka Jacob Maiseli
doaj   +1 more source

COMPARISON OF MISSING VALUE IMPUTATION USING MEAN, BAYESIAN KNN, AND NON-BAYESIAN KNN ON TEP GENE EXPRESSION DATA

Media Statistika
Analysis of gene expression data, particularly in cancer data, often faces challenges due to the presence of missing values. One approach to overcome this is data imputation.
Mastika Mastika, Titin Siswantining, Alhadi Bustamam   +2 more
doaj   +1 more source

Size Constrained Clustering With MILP Formulation

IEEE Access, 2020
Clustering is one of the essential tools for data mining since it reveals the natural structures of the unlabeled data. Many clustering algorithms have been proposed in the last decades.
Wei Tang, Yang Yang, Lanling Zeng, Yongzhao Zhan   +3 more
doaj   +1 more source