scholarly journals Forecasting and Mapping Coffee Borer Beetle Attacks Using GSTAR-SUR Kriging and GSTARX-SUR Kriging Models

2020 ◽  
Vol 11 (2) ◽  
pp. 65-73
Author(s):  
Henny Pramoedyo ◽  
Arif Ashari ◽  
Alfi Fadliana
Keyword(s):  
Spatial Clustering ◽  
Kriging Model ◽  
Seemingly Unrelated Regression ◽  
Kriging Interpolation ◽  
Percentage Error ◽  
Eastern Region ◽  
Unrelated Regression ◽  
Interpolation Technique ◽  
The Mean

The research aimed to use Generalized Space Time Autoregressive (GSTAR) and GSTARX modeling with the Seemingly Unrelated Regression (SUR) approach and combine them with the Kriging interpolation technique in an unobserved location. The case study was coffee borer beetle forecasting in Probolinggo Regency, East Java, Indonesia, with Watupanjang Village as the unobserved location. The results show that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can predict coffee borer beetle attacks in unobserved areas with high accuracy. It is indicated by the Mean Absolute Percentage Error (MAPE) values of less than 10%. The addition of exogenous variables (rainfall) into the model is proven to improve the accuracy of the model. The Root-Mean-Square Error (RMSE) value of the GSTARX-SUR Kriging model is smaller than the GSTAR-SUR Kriging model. The structure of the model produced from the research, GSTARX-SUR (1,[1,12])(10,0,0), can be used as a reference in modeling coffee borer beetle attacks in other regencies. Map of forecasting coffee borer beetle attack shows that the spread of coffee borer beetle attack is spatial clustering with the attack center located in the eastern region of Probolinggo Regency.

scholarly journals INTERPOLASI KRIGING DALAM PEMODELAN GSTAR-SUR DAN GSTARX-SUR PADA SERANGAN HAMA PENGGEREK BUAH KOPI

2020 ◽  
Vol 13 (1) ◽  
pp. 25-35
Author(s):  
Henny Pramoedyo ◽  
Arif Ashari ◽  
Alfi Fadliana
Keyword(s):  
Forecast Accuracy ◽  
Kriging Model ◽  
Complete Data ◽  
Kriging Interpolation ◽  
Coffee Berry ◽  
Coffee Berry Borer ◽  
Interpolation Technique

The GSTAR and GSTARX models normally can only be formed from observed locations. The problem that sometimes occurs is that not all locations that want to be modeled have complete data as well as other locations. This study uses GSTAR and GSTARX modeling using SUR approach and combines them with the kriging interpolation technique for forecasting coffee berry borer attack in Probolinggo Regency. This modeling is called GSTAR-SUR Kriging and GSTARX-SUR Kriging. This study aims to determine the best model between GSTAR-SUR Kriging and GSTARX-SUR Kriging for forecasting coffee borer attack in an unobserved location. The result of this study shows that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can be used for forecasting coffee berry borer attack in unobserved locations with high forecast accuracy shown by MAPE values <10%. In this study the GSTARX-SUR Kriging model (1,[1,12])(10,0,0) is the best model for forecasting boffee berry borer attacks in unobserved locations.

scholarly journals Bayesian Skew Normal Seemingly Unrelated Regression Modelling of Gross Regional Domestic Product

2018 ◽  
pp. 869-879
Author(s):  
Agus Budi Santosa ◽  
Nur Iriawan ◽  
Setiawan Setiawan ◽  
Mohammad Dokhi
Keyword(s):  
Bayesian Approach ◽  
Absolute Error ◽  
Seemingly Unrelated Regression ◽  
Percentage Error ◽  
Actuarial Science ◽  
Normal Distributions ◽  
Unrelated Regression ◽  
Normal Error ◽  
Skew Distribution ◽  
Skew Normal

The assumption of the error normality in the regression model was often questioned especially in cases where there was an outlier, which causes the behavior of asymmetric data. To overcome this, without data transformation, we could use skew distribution. This distribution was very important and applicable in various fields of science such as finance, economics, actuarial science, medicine, biology, investment. Skew Normal distributions had been proven to have a convenient for calculating bias in data with asymmetric behavior. This study aims to model SUR with Skew Normal error using Bayesian approach applied to East Java GRDP data. This study would compared two types of models, namely models with Normal distributed errors and models with Skew Normal distributed errors. The result of parameter estimation with Bayesian approach shows that SUR Skew Normal model was more suitable for East Java GRDP modeling rather than using normal error model. This was based on their smaller Root of Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) value. 

scholarly journals Improved Liu-Type Estimator of Parameters in Two Seemingly Unrelated Regressions

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Jibo Wu
Keyword(s):  
Parameter Estimation ◽  
Mean Square Error ◽  
Seemingly Unrelated Regression ◽  
Mean Square ◽  
Seemingly Unrelated Regressions ◽  
Unrelated Regression ◽  
The Mean

We consider the parameter estimation in two seemingly unrelated regression systems. To overcome the multicollinearity, we propose a Liu-type estimator in seemingly unrelated regression systems. The superiority of the new estimator over the classic estimator in the mean square error is discussed and we also discuss the admissibility of the Liu-type estimator.

scholarly journals A New Liu Type of Estimator for the Restricted SUR Estimator

2020 ◽  
Vol 18 (1) ◽  
pp. 2-11
Author(s):  
Kristofer Månsson ◽  
B. M. Golam Kibria ◽  
Ghazi Shukur
Keyword(s):  
Linear Subspace ◽  
Mean Squared Error ◽  
Shrinkage Estimator ◽  
Seemingly Unrelated Regression ◽  
Simulation Techniques ◽  
Squared Error ◽  
Unrelated Regression ◽  
The Mean

A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived. The new estimator may have a lower MSE than the traditional estimators. It was shown using simulation techniques the new shrinkage estimator outperforms the commonly used estimators in the presence of multicollinearity.

An Evaluation of the Use of Ordinary Least Squares for Estimating Tourism Demand Models

1997 ◽  
Vol 35 (4) ◽  
pp. 69-73 ◽  
Author(s):  
Clive L. Morley
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Percentage Error ◽  
Regression Estimation ◽  
Tourism Demand ◽  
Constant Variance ◽  
Unrelated Regression ◽  
Demand Models ◽  
The Common

Estimating tourism demand models involves a set of related equations with errors that may not satisfy the common assumptions of being independent, with constant variance and normal distribution. In such circumstances, seemingly unrelated regression estimation may be considered a better estimation technique than ordinary least squares. Results from a simulation exercise, however, show that generally there is little difference between ordinary least squares and seemingly unrelated regression. The ordinary least squares technique performs well, and the results give little reason to use more complex estimation techniques. Another feature of tourism data is that strong growth in tourist numbers is often observed. This feature implies that models in which such series are the dependent variable are not consistently estimated by least squares methods. A percentage error loss function is proposed as a more appropriate criterion for estimating tourist data of this type.

Preserving correlation while modelling diameter distributions

2004 ◽  
Vol 34 (1) ◽  
pp. 221-232 ◽  
Author(s):  
A Robinson
Keyword(s):  
Mixed Effects ◽  
Seemingly Unrelated Regression ◽  
Mixed Effects Models ◽  
Diameter Distribution ◽  
Distribution Models ◽  
Unrelated Regression ◽  
Simultaneous Prediction ◽  
Components Analysis ◽  
Diameter Distributions

The construction of diameter-distribution models sometimes calls for the simultaneous prediction of population parameters from hierarchical data. Appropriate data for such models have characteristics that should be preserved or accommodated: clustering and contemporaneous correlations. Fitting techniques for such data must allow for these characteristics. Using a case study, I compare two techniques — seemingly-unrelated regression (SUR) and principal components analysis (PCA) — whilst using mixed-effects models. I adapt and apply a metric that focuses on volume prediction, which is a key application for diameter distributions. The results suggest that using mixed-effects models provides useful insights into environmental variation, and that SUR is more convenient and produces a slightly better fit than PCA. Both techniques are acceptable with regard to regression assumptions.

scholarly journals A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China

2019 ◽  
Vol 8 (11) ◽  
pp. 495 ◽  
Author(s):  
Xiong ◽  
Li ◽  
Cheng ◽  
Ye ◽  
Zhang
Keyword(s):  
Regression Models ◽  
Population Distribution ◽  
Percentage Error ◽  
Night Time ◽  
Geographically Weighted Regression Model ◽  
Southwestern Area ◽  
The Mean ◽  
Gwr Model ◽  
Better Than

Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.

scholarly journals Using the Optimization Algorithm to Evaluate the Energetic Industry: A Case Study in Thailand

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 87 ◽  
Author(s):  
Chia-Nan Wang ◽  
Tien-Muoi Le ◽  
Han-Khanh Nguyen ◽  
Hong Ngoc-Nguyen
Keyword(s):  
Rate Of Change ◽  
Productivity Index ◽  
Percentage Error ◽  
Data Envelopment ◽  
The Past ◽  
Future Performance ◽  
Forecast Data ◽  
The Mean ◽  
Efficiency And Productivity

Thailand’s economy is developing rapidly, with energy being a significant factor in this development. This study uses a variety of models to assess the performance of Thailand’s energy industry in two different phases, the first being from 2013 to 2017 and the second from 2018 to 2020. The Malmquist model-one of data envelopment required input and output data that showed Thailand’s productivity index and the rate-of-change ratio, which is used to assess technical changes, change efficiency, and productivity changes of the 12 listed companies in energetic generation and distribution in Thailand. To calculate future indicators, the forecast data are generated by applying the Grey model (1,1) GM(1,1). Accuracy prediction is determined by the mean absolute percentage error (MAPE). The results show that the magnitude of the change in efficiency during the first period is stable, and some major changes in the technical level of some companies may be observed. In the future, the performance of most companies has increased steadily, but performance has been outstanding. This research provides insights into Thailand’s energy over the past few years, and predictions of future performance may be used as a reference for more purposes.

scholarly journals Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4839
Author(s):  
Aritz Bilbao-Jayo ◽  
Aitor Almeida ◽  
Ilaria Sergi ◽  
Teodoro Montanaro ◽  
Luca Fasano ◽  
...  
Keyword(s):  
Behavior Modeling ◽  
Percentage Error ◽  
Location Prediction ◽  
Indoor Environments ◽  
Prediction System ◽  
Indoor Location ◽  
The Mean ◽  
Locating System ◽  
Monitoring Devices ◽  
Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

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