The Weibull distribution is regarded as among the finest in the family of failure distributions.One of the most commonly used parameters of the Weibull distribution(WD)is the ordinary least squares(OLS)technique,which...The Weibull distribution is regarded as among the finest in the family of failure distributions.One of the most commonly used parameters of the Weibull distribution(WD)is the ordinary least squares(OLS)technique,which is useful in reliability and lifetime modeling.In this study,we propose an approach based on the ordinary least squares and the multilayer perceptron(MLP)neural network called the OLSMLP that is based on the resilience of the OLS method.The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers,and eases the difficulty of determining weights in case of the weighted least square(WLS).Another method is proposed by incorporating a weight into the general entropy(GE)loss function to estimate the parameters of the WD to obtain a modified loss function(WGE).Furthermore,a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation(BLWGE)by using a weighted GE loss function.The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes.In addition,the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods,in terms of the mean squared error and requirements related to time.展开更多
Air pollution is one of the crucial environmental challenges facing the countries of the Economic Community of West African States (ECOWAS). The objective of this paper is to examine the effect of an attractive tax po...Air pollution is one of the crucial environmental challenges facing the countries of the Economic Community of West African States (ECOWAS). The objective of this paper is to examine the effect of an attractive tax policy on the relationship between Foreign Direct Investment (FDI) and air pollution in ECOWAS region over the period 2000 to 2019. By using the Ordinary Least Squares (OLS) method and panel data analyses (fixed effects and random effects), the results show that, in general, FDI does not have a significant effect on air pollution in the region. However, closer analysis reveals that an interaction between FDI and an attractive tax policy has a negative effect on air quality, leading to an increase in air pollution. Thus, companies attracted by tax incentives may not meet rigorous environmental standards. These results highlight the importance for policymakers to balance economic incentives with environmental protection in ECOWAS. Attractive tax policies can stimulate investment, but they must be designed in a way that encourages environmentally friendly practices, thereby helping to improve air quality in the region.展开更多
Bayesian quantile regression has drawn more attention in widespread applications recently. Yu and Moyeed (2001) proposed an asymmetric Laplace distribution to provide likelihood based mechanism for Bayesian inference ...Bayesian quantile regression has drawn more attention in widespread applications recently. Yu and Moyeed (2001) proposed an asymmetric Laplace distribution to provide likelihood based mechanism for Bayesian inference of quantile regression models. In this work, the primary objective is to evaluate the performance of Bayesian quantile regression compared with simple regression and quantile regression through simulation and with application to a crime dataset from 50 USA states for assessing the effect of potential risk factors on the violent crime rate. This paper also explores improper priors, and conducts sensitivity analysis on the parameter estimates. The data analysis reveals that the percent of population that are single parents always has a significant positive influence on violent crimes occurrence, and Bayesian quantile regression provides more comprehensive statistical description of this association.展开更多
In case of heteroscedasticity, a Generalized Minimum Perpendicular Distance Square (GMPDS) method has been suggested instead of traditionally used Generalized Least Square (GLS) method to fit a regression line, with a...In case of heteroscedasticity, a Generalized Minimum Perpendicular Distance Square (GMPDS) method has been suggested instead of traditionally used Generalized Least Square (GLS) method to fit a regression line, with an aim to get a better fitted regression line, so that the estimated line will be closest one to the observed points. Mathematical form of the estimator for the parameters has been presented. A logical argument behind the relationship between the slopes of the lines and has been placed.展开更多
Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness ...Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region.展开更多
Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor...Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2. Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2. Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled展开更多
This study was aimed to analyze teff (Eragrostis tef) market chain in south west Shoa zone with objective of factors affecting teff market supply using two stage ordinary least square approaches. The majority of Ethio...This study was aimed to analyze teff (Eragrostis tef) market chain in south west Shoa zone with objective of factors affecting teff market supply using two stage ordinary least square approaches. The majority of Ethiopia’s population earns its livelihood primarily from agriculture. Cereals teff is the first in Ethiopia area coverage and production. Teff (Eragrostis tef) is a major staple food crop in Ethiopia. Both primary and secondary data were used in this study. Primary data was collected from 138 sampled farmers and 38 traders from both districts by using semi-structured interview. The OLS (ordinary least square) model results showed that seven explanatory variables significantly affected the quantity of teff supplied to the market supplied by smallholder producers. Age, education level and current market price were negatively and significantly affecting teff market supply. Distance to the nearest market, farm size, perception and quantity produced were positively and significantly influencing marketed supply of teff. Policy implications that were to take place highly recommendation those are relevant to improve teff marketing system in the study area which indicated production and market orientation were set based on the significant variables and raised problems by the stakeholders. To improve market supply of teff in the study area resolving the prevailing production problems deems a necessary condition.展开更多
基金The authors are grateful to the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University Supporting Project Number(2020/01/16725),Prince Sattam bin Abdulaziz University,Saudi Arabia.
文摘The Weibull distribution is regarded as among the finest in the family of failure distributions.One of the most commonly used parameters of the Weibull distribution(WD)is the ordinary least squares(OLS)technique,which is useful in reliability and lifetime modeling.In this study,we propose an approach based on the ordinary least squares and the multilayer perceptron(MLP)neural network called the OLSMLP that is based on the resilience of the OLS method.The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers,and eases the difficulty of determining weights in case of the weighted least square(WLS).Another method is proposed by incorporating a weight into the general entropy(GE)loss function to estimate the parameters of the WD to obtain a modified loss function(WGE).Furthermore,a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation(BLWGE)by using a weighted GE loss function.The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes.In addition,the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods,in terms of the mean squared error and requirements related to time.
文摘Air pollution is one of the crucial environmental challenges facing the countries of the Economic Community of West African States (ECOWAS). The objective of this paper is to examine the effect of an attractive tax policy on the relationship between Foreign Direct Investment (FDI) and air pollution in ECOWAS region over the period 2000 to 2019. By using the Ordinary Least Squares (OLS) method and panel data analyses (fixed effects and random effects), the results show that, in general, FDI does not have a significant effect on air pollution in the region. However, closer analysis reveals that an interaction between FDI and an attractive tax policy has a negative effect on air quality, leading to an increase in air pollution. Thus, companies attracted by tax incentives may not meet rigorous environmental standards. These results highlight the importance for policymakers to balance economic incentives with environmental protection in ECOWAS. Attractive tax policies can stimulate investment, but they must be designed in a way that encourages environmentally friendly practices, thereby helping to improve air quality in the region.
文摘Bayesian quantile regression has drawn more attention in widespread applications recently. Yu and Moyeed (2001) proposed an asymmetric Laplace distribution to provide likelihood based mechanism for Bayesian inference of quantile regression models. In this work, the primary objective is to evaluate the performance of Bayesian quantile regression compared with simple regression and quantile regression through simulation and with application to a crime dataset from 50 USA states for assessing the effect of potential risk factors on the violent crime rate. This paper also explores improper priors, and conducts sensitivity analysis on the parameter estimates. The data analysis reveals that the percent of population that are single parents always has a significant positive influence on violent crimes occurrence, and Bayesian quantile regression provides more comprehensive statistical description of this association.
文摘In case of heteroscedasticity, a Generalized Minimum Perpendicular Distance Square (GMPDS) method has been suggested instead of traditionally used Generalized Least Square (GLS) method to fit a regression line, with an aim to get a better fitted regression line, so that the estimated line will be closest one to the observed points. Mathematical form of the estimator for the parameters has been presented. A logical argument behind the relationship between the slopes of the lines and has been placed.
文摘Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region.
文摘Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2. Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2. Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled
文摘This study was aimed to analyze teff (Eragrostis tef) market chain in south west Shoa zone with objective of factors affecting teff market supply using two stage ordinary least square approaches. The majority of Ethiopia’s population earns its livelihood primarily from agriculture. Cereals teff is the first in Ethiopia area coverage and production. Teff (Eragrostis tef) is a major staple food crop in Ethiopia. Both primary and secondary data were used in this study. Primary data was collected from 138 sampled farmers and 38 traders from both districts by using semi-structured interview. The OLS (ordinary least square) model results showed that seven explanatory variables significantly affected the quantity of teff supplied to the market supplied by smallholder producers. Age, education level and current market price were negatively and significantly affecting teff market supply. Distance to the nearest market, farm size, perception and quantity produced were positively and significantly influencing marketed supply of teff. Policy implications that were to take place highly recommendation those are relevant to improve teff marketing system in the study area which indicated production and market orientation were set based on the significant variables and raised problems by the stakeholders. To improve market supply of teff in the study area resolving the prevailing production problems deems a necessary condition.
