Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as and . Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, (using robust and non-robust measures), and our proposed and plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust and robust were not very successful in detecting IOs. The suffered from the masking effect, and the robust suffered from swamping in general spatial models. Interestingly, the results showed that the proposed plot, followed by the plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.

Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

Influential Observations, which are outliers in x direction, y direction or both, remain a hitch in classical regression model fitting. Spatial regression model, with peculiar nature of outliers due to their local nature, is not free from the effect of such influential observations. Researchers have adapted some classical regression techniques to the spatial models and yielded satisfactory results. However, masking or/and swamping remain stumbling block to such methods. We obtained the spatial representation of the classical regression measures of diagnostic in general spatial model. Commonly used diagnostic measure in spatial diagnostic, the Cook's distance, is compared to some robust methods, Hi2 (using robust and non-robust measures), and classification based on generalized residuals and diagnostic generalized potentials, ISRs-Posi and ESRs-Posi, with the help of the obtained spatial prediction residuals and the spatial leverage term. Results of simulation and applications to real data have shown the advantage of the ISRs-Posi and ESRs-Posi due to classification of outliers over Cook's distance and non-robust Hsi12, which suffer from masking, and robust Hsi22 which suffer from swamping in general spatial model.

On the variability of technological indicators in the extraction of precious metals

in the conditions of significant variability of processed polymetallic ores of the Akbastau Deposit, it is essential to minimize the variability of technological indicators of enrichment. Due to the multifactorial nature and non-linearity of the flotation process, the use of classical regression models does not provide the necessary level of reliability, therefore, there is a significant variability in the extraction of precious metals. To solve this problem, the paper substantiates the use of the neural network modeling methodology, which allows to estimate the variability of gold and silver extraction depending on the variation of the content of metals in the ore.

Pengaruh Tingkat Hunian Kamar dan Pendapatan Spa terhadap Laba Operasional di Nusa Dua Beach Hotel & Spa

The purpose of this research is to determine the effect of room occupancy and spa revenue on operating profit at Nusa Dua Beach Hotel & Spa. The data analysis technique used was descriptive quantitative, and analyzed with the test of classical regression assumption, analysis multiple linear regression, individual parameter significance test (t test), simultaneous significance test (Test F) and coefficient of determination R2. Report of room occupancy, spa revenue and operating profit at Nusa Dua Beach Hotel & Spa period 2017 to 2019. The results of this research indicate that the room occupancy and spa revenue have a positive and significant effect on operating profit with the regression equation Y = -484,794,904,574 + 28,760,823,190X1 + 3,260X2 and determination coefficient is 70,2%. Room occupancy and spa revenue partially and simultaneously have a significant effect on operating profit at Nusa Dua Beach Hotel & Spa.

A note non Nonparametric Regression Modeling using a Density Function

The nonparametric regression offers alternative to classical regression analysis when the data are not well behaved or when the classical regression model shows significant lack of fit. In recent years, It has been applied using Kernel estimators and the smoothing splines, but these methods wields some bias of estimation. In this study, a semi-parametric multiplicative bias reduction density function was used to develop a non parametric regression model. Simulation studies conducted showed that the proposed estimator performs better than both the Kernel and the smoothing splines estimators especially with large samples

ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI PREVALENSI KETIDAKCUKUPAN KONSUMSI PANGAN DI SUMATERA BARAT MENGGUNAKAN GEODA

Background: Daily energy requirements must be fulfilled for a healthy and active life. Prevalence of Undernourishment (PoU) is an indicator which are developed for goal. A number of factors were expected affecting PoU were tested in this study. We also tested the possibility of these factors could have a spatial correlationObjectives: The study produced a map of the spread of PoU and the factors which were influenced them (independent variables). The study need to yield the best estimation model.Method : This study produce spread map of all variables, for visible purposes, and the classical regression were made. All OLS assumption will be test. Then, SAR and SEM models will be made. Finally, the best model for this study will be chosen. GeoDa software helps all steps.Results : This study concludes PoU has positive auto correlation and growth has a negative autocorrelation. The best model which produced is the Spatial Error Model (SEM). The slow trend of PoU in West Sumatera Barat is strongly suspected due to the habit of West Sumatera residents in consuming high-calorie foods such as rendang, curry and coconut milk.

The calibration of the hailpads upon the Greek National Hail Suppression Program, using the Classical and Inverse Regression methods

The hailpad, constructed from a plate of Styrofoam, is a simple instrument for recording hailfall. In addition to simply recording the hailfall, calibration of the instrument is required to obtain quantitative measurements of the hail. The calibration is a process leading to a calibration equation, a polynomial establishing a relationship between the diameter of a hailstone and the dent the hailstone is left on the surface of the hailpad. A hailpad network, consisted of 154 instruments, has established inGreece, in the context of the Greek National Hail Suppression Program operating for the protection of the agricultural cultivations from hail damage. For the calibration of the haipads of the network the well known “Energy Matching technique” has adopted and the Inverse Regression method is applied from the beginning for the obtainment of the calibration equation. In the present study along with the Inverse Regression method hitherto applied, the Classical Regression method is examined and presented and inferential statistics are also introduced in both methods in order to establish a more stringent statistical procedure for the calibration of the hailpads. After the theoretical analysis the data from a calibration experiment were analyzed, calibration models obtained using both methods of regression, hail diameters were predicted with the two models when new observations were available and the results compared to each other. The comparison of the two models' predictions showed that the results are almost the same so there is no good reason to replace the hitherto Inverse Regression method. However, it would be good to introduce the Classical Regression method alongside the Inverse. In addition, prediction bands for both methods should be introduced giving to the results the range of the confidence interval of the predictions.