Number Of Explanatory Variables Research Articles

Why Does a Child Labor Exist in Aceh Province, Indonesia?

Child labor is a pervasive issue around the world, especially in developing countries. It is a wide phenomenon issue in which many countries have a similar problem related to child labor. The purpose of this study is to examine the factors determining child labor in Aceh Province. The data used is the primary data of the National Social Economic Survey (SUSENAS) 2016 conducted by the Central Bureau of Statistics. The dependent variable is children aged 10-17 years, while the independent variables are the educational level, the role of child as the head of the household, gender, residence area, single parent status, and age. The logistic regression model is used to describe the relationship between the independent variable (child labor and not child labor) with a number of explanatory variables. The result indicates that there is a relationship between child labor and all variables used in this model. Nonetheless, status as head of household and single parent woman are irrelevant as the significance value (α) is less than 5% by using the Wald Test Statistic. Thus, it is recommended to do some improvements in young people’s education system. Moreover, government policies that protect the human rights of child labors need to be done as a form of government intervention.

Modified ridge parameter estimators for log-gamma model: Monte Carlo evidence with a graphical investigation

In ordinary ridge regression, the estimation of the ridge parameter is a significant topic. This article generalizes some different methods for estimating the ridge parameter by considering the work of Kibria (2003), Khalaf and Shukur (2005), Alkhamisi, Khalaf, and Shukur (2006), Alkhamisi and Shukur (2008) and Muniz et al. (2012) in generalized linear models. The superiority of these estimators are assessed by the estimated mean squared error via Monte Carlo simulation study and real data set where the response follows gamma distribution with log link function. In this simulation study, it is chosen to vary the degree of correlation, the sample size, the number of explanatory variables and the different shape parameter for gamma response.

A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

In this paper, we deal with the optimization method of ridge parameters in a generalized ridge regression by minimizing a model selection criterion (MSC). The optimization methods based on minimizations of generalized Cp criterion and GCV criterion are fast because the minimizers of the two criteria can be derived as closed forms. Although the methods are fast, they do not work well when the number of explanatory variables is larger than the sample size. Methods based on minimizations of other MSCs may work well even if the number of explanatory variables is larger than the sample size and are not fast because the minimizers of the MSCs have not been derived as closed forms. Even though a minimization problem of MSC cannot be solved analytically, we propose a fast optimization algorithm to minimize MSC by specifying the small number of minimizer candidates. By conducting numerical examinations, we compare performances of the optimization methods based on the minimizations of MSCs.

Regional disparities in access to higher education in Greece

This study examines the geographical dimension of access to higher education in Greece by mapping spatial disparities in the distribution of vathmos prosvasis, the decisive score for being accepted in one of the country’s tertiary education departments. Influenced by the ‘spatial turn’ in the humanities and the social sciences, this paper takes a geographical approach. Spatial autocorrelation indices (Moran’s I) show that examinees from mountainous settlements, insular areas, and inner city neighbourhoods are much less likely to achieve a score that would allow them to enter one of the country’s higher educational institutions. The authors use the ‘oval’ metaphor in order to describe these differences. A number of explanatory variables at school catchment area level reduce the variation in vathmos prosvasis, but no conclusions can be made with regards to casual relationships between school neighbourhood characteristics and individual scores. The alarmingly low scores in a specific geographical region, the so-called ‘Pomakohoria’, are discussed as an example of how access to higher education in Greece is being affected by location.

Poverty and Income Inequality in Rural Agrarian Household of Southwestern Nigeria: The Gender Perspective

Introduction:Poverty is a common phenomenon in the world today, with a vast dominance in Africa, with Nigeria not exempted. This paper analyzes gender perspective of income inequality and poverty among sample of rural households in Southwest, Nigeria. Gini coefficient, Foster-Greer-Thorbecke (FGT) and Logistic regression model was used to assess the objective of the study.Methods:The poverty line stands at N15271.83 ($76.74), with more than half (53.71%) of the male population considered poor while the female poor population stood at 47.22%. However, the study found that income inequality was lower among the male respondents than the female counterparts. A number of explanatory variables were considered, the following - educational years, household size, farming experience, market distance, extension access, credit access and member of social group represent important poverty drivers in the study area.Results and ConclusionThe study therefore suggests that reducing the number of dependent household members and ensuring ready availability and equal access to institutional facilities, basic amenities, credit facilities, and human capital development of rural households are some measures that could curb the menace of poverty.

Estimation of a rank-reduced functional-coefficient panel data model with serial correlation

We consider estimation of a functional-coefficient panel data model. This model is useful for modeling time varying and cross-sectionally heterogeneous relationships between economic variables. We allow for serial correlation and heteroscedasticity in the model. When the number of explanatory variables is large, we impose a rank-reduced structure on the model’s functional coefficients to reduce the number of functions to be estimated and thus improve estimation efficiency. To adjust for serial correlation and further improve estimation efficiency, we use a Cholesky decomposition on the serial covariance matrices to produce a transformation of the original panel data model. By applying the standard semiparametric profile least squares method to the transformed model, more efficient estimates of the coefficient functions can be obtained. Under some regularity conditions, we derive the asymptotic distribution for the developed semiparametric estimators and show their efficiency improvement under correct specification of the serial covariance matrices. To attain this efficiency gain when the serial covariance structure is unknown, we propose approaches to consistently estimate the lower triangular matrix in the Cholesky decomposition for balanced panel data, and the serial covariance matrices for unbalanced panel data. Numerical studies, including Monte Carlo experiments and an empirical application to economic growth data, show that the developed semiparametric method works reasonably well in finite samples.

CORRELATION AND REGRESSIONAL ANALYSIS OF THE DEPENDENCE OF ENTERPRISE RECEIPTS FROM THE FOREIGN ECONOMIC ACTIVITY FACTORS

A correlation-regression analysis of the company's revenue dependence on the foreign economic activity factors was performed using the example of the Center for Appraisal and Analytical Technologies “Analytics.” The employees number (x1), rate $ (x2), rate € (x3), number of new open representative offices and branches for the period (x4) and service names in the company's nomenclature at the end of the period (x5) and number of suppliers at the end of the period (x6) revenue from the sale of medical equipment for the period (x7) and revenues from equipment maintenance and repair activities for the period (x8) and revenues from other activities for the period (x9), customs duties on equipment imports for the period (x10) and imports for the period (x11) and number of protectionism measures applied to the Russian Federation for the period (х12) and the number of measures of liberalization of foreign economic activity applied to the Russian Federation for the period (x13) was chosen as an external factor. The number of explanatory variables was reduced from 12 to 7 after the logical relationship between the factors analysis. Correlation analysis showed that many variables are intercorrelated. So, 8 regression models were constructed to avoid the negative effect of multicollinearity. These models do not contain strongly related factors. Statistical measures such as confidence intervals, the coefficient of determination, the Student’s index and the Fisher coefficient are given to assess the quality of the constructed models. It is shown that the constructed regression models are statistically significant. It’ll become possible to predict the results of the activity of the Center for Information Technologies "Analytics" in future periods if we used the regression models found. Also we could to minimize the risks due to the determination of the company's sources of income which determines the practical significance of the work.

Central Composite Experimental Designs for Multiple Responses With Different Models

ABSTRACTCentral composite designs (CCDs) are widely accepted and used experimental designs for fitting second-order polynomial models in response surface methods. However, these designs are based only on the number of explanatory variables being investigated. In a multiresponse problem where prior information is available in the form of a screening experiment or previous process knowledge, investigators often know which factors will be used in the estimation of each response. This work presents an alternative design based on CCDs that allows main effects to be aliased for factors that are not related to the same response. This results in fewer required runs than current designs, saving investigators both time and money, by taking this prior information into account. R-package “DoE.multi.response” is included as a supplement for constructing these designs.

Estimation of semiparametric regression model with right-censored high-dimensional data

ABSTRACTIn this paper, we consider the estimation problem for the semiparametric regression model with censored data in which the number of explanatory variables p in the linear part is much larger than sample size n, often denoted as p n. The purpose of this paper is to study the effects of covariates on a response variable censored on the right by a random censoring variable with an unknown probability distribution. It should be noted that high variance and over-fitting are a major concern in such problems. Ordinary statistical methods for estimation cannot be applied directly to censored and high-dimensional data, and therefore a transformation is required. In the context of this paper, a synthetic data transformation is used for solving the censoring problem. We then apply the LASSO-type double-penalized least squares (DPLS) to achieve sparsity in the parametric component and use smoothing splines to estimate the nonparametric component. A Monte Carlo simulation study is performed to show the performance of the estimators and to analyse the effects of the different censoring levels. A real high-dimensional censored data example is used to illustrate the ideas discussed herein.

Penalized negative binomial models for modeling an overdispersed count outcome with a high-dimensional predictor space: Application predicting micronuclei frequency.

Chromosomal aberrations, such as micronuclei (MN), have served as biomarkers of genotoxic exposure and cancer risk. Guidelines for the process of scoring MN have been presented by the HUman MicroNucleus (HUMN) project. However, these guidelines were developed for assay performance but do not address how to statistically analyze the data generated by the assay. This has led to the application of various statistical methods that may render different interpretations and conclusions. By combining MN with data from other high-throughput genomic technologies such as gene expression microarray data, we may elucidate molecular features involved in micronucleation. Traditional methods that can model discrete (synonymously, count) data, such as MN frequency, require that the number of explanatory variables (P) is less than the number of samples (N). Due to this limitation, penalized models have been developed to enable model fitting for such over-parameterized datasets. Because penalized models in the discrete response setting are lacking, particularly when the count outcome is over-dispersed, herein we present our penalized negative binomial regression model that can be fit when P > N. Using simulation studies we demonstrate the performance of our method in comparison to commonly used penalized Poisson models when the outcome is over-dispersed and applied it to MN frequency and gene expression data collected as part of the Norwegian Mother and Child Cohort Study. Our countgmifs R package is available for download from the Comprehensive R Archive Network and can be applied to datasets having a discrete outcome that is either Poisson or negative binomial distributed and a high-dimensional covariate space.

Factors influencing trip generation on metro system in Madrid (Spain)

In the last few years, ordinary least squares (OLS) models have been the most used regression models for estimating trips in urban areas. However, these models present some limitations, whose the most relevant is that they ignore the geographic variations of relationships among variables. On the other hand, the explanatory power of trip generation models can be enhanced using geographically weighted regression (GWR), which is a local form of linear regression used to model spatially varying relationships.In this paper, factors affecting daily trips made by the Metro system of Madrid (Spain) are analysed through a GWR model. In order to evaluate the factors mainly influencing daily trip generation, a number of explanatory variables, including socio-economic characteristics of the population, land use, accessibility, and transportation system attributes, were considered. The analysis led to the identification of seven explanatory variables to be included in the model specification.The GWR results captured the spatial variation of the relationships among the variables across the study region. The research study attempted to identify the variable that most influenced trip generation for different parts of the city through a comparison of GWR results between various city zones.

Gravity modeling in social science: the case of the commuting phenomenon in Greece

This article empirically examines the utility of the gravity modeling in regard to its explanatory resolution, to its power against linearity, and to its geographical scale (intercity vs. interregional). For this purpose, the analysis is performed towards three directions; the first regards the model structure and examines how the number of explanatory variables (drafted from a pool of available predictors) affects the model determination, the second examines the model type, comparing the same predictor configurations entered to gravity multivariate linear regression models, and the third examines the changes induced corresponding interregional and intercity gravity models due to the effect of geographical scale. The analysis shows that the gravity modeling is generally effective in the study of systems of spatio-economic interaction, where the use of its standard expression appears a safe and simple choice. However, this model may also attain many effective extended expressions, and thus, it can be functional in higher complexity demand. Overall, this paper sheds some light to the interregional commuting for the case of Greece and highlights the utility of gravity model in the fields of Econophysics and of economic modeling.

Prediction Intervals of Response Variables based on Quantiles in High Dimensional Regression Analyses

In regression analyses it is of interest to obtain prediction intervals of the response variables. However, such prediction intervals are not obvious if the number of explanatory variables exceeds the number of observations since the least square method cannot be used in this case. This paper discusses the problems of constructing prediction intervals in high dimensional regression models, in which the number of explanatory variables is greater than the number of observations. A quantile approach is proposed to construct such intervals and it has been evaluated by means of simulation. In this approach pairs of quantiles based on the certain probability are specified and followed by evaluation to obtain the shortest interval. Since the number of explanatory variables was large then several techniques to select the variables were employed. These techniques were the best subset regression, LASSO (Least Absolute Shrinkage and Selection Operator) regression, and model averaging. The simulation data was generated according to two different scenarios. The first scenario was designed for models having symmetric error distributions whereas the second scenario was designed for models with non-symmetric error distributions. The simulation results showed that in the case of symmetric error distributions all of the regression methods mentioned above produced similar prediction intervals, except the LASSO regression. However, in the case of non-symmetric error distributions it has been evidence that model averaging has provided the best prediction intervals when compared with the best subset and LASSO regressions although has wide range of intervals. This revealed that model averaging can be used to predict the response variables in high-dimensional regression analyses although the data is non-symmetrically distributed.

A Combined Modeling of Generalized Linear Mixed Model and LASSO Techniques for Analizing Monthly Rainfall Data

The rainfall pattern is always interesting to be investigated. This paper discusses the performance of three methods in modeling the rainfall data namely the LASSO (Least Absolute Shrinkage and Selection Operator) and GLMM (Generalized Linear Mixed Model) methods as well as a combination of GLMM and LASSO techniques. The rainfall data is usually collected on a regular basis, hence it is longitudinal data. The GLMM methods are usually employed to analyze longitudinal data, especially when number of explanatory variables is small. If the number of explanatory variables is large and if these variables are correlated then the GLMM estimation will be suffered by ill condition problems. These problems may be overcome by adding L1 penalty and start doing variable selection and shrinkage simultaneously. In this paper a combination of GLMM and LASSO techniques is evaluated by using monthly rainfall data, a high dimensional data, collected during 1981-2014 in Indramayu sub-district. The results showed that a combination of GLMM and LASSO methods is superior when compared with GLMM and LASSO methods separately. This claim is supported by evidence that MSE of the combined method is smaller than MSEs of the other two methods for various λ (lambda).

Estimating the very fine sand fraction for calculating the soil erodibility K‐factor

AbstractThe K‐factor of the universal soil loss equation is a core component in many erosion models, as a measure of soil erodibility. It can be estimated by a nomograph, where the summed fractions of silt and very fine sand (VFS) are basic inputs. Frequently, only the three broad particle‐size classes of sand, silt, and clay are measured in laboratories; thus, the VFS fraction must be estimated. Three models are currently available for this estimation, namely, (a) the Revised Universal Soil Loss Equation formula, (b) the European Soil Data Centre method, and (c) the Shirazi–Boersma theory, all three use just the sand fraction as explanatory variable. Nevertheless, their accuracy has never been assessed, and this is the main purpose of this study. The data used to test the VFS estimation methods were drawn from the National Cooperative Soil Survey Soil Characterization Database, incorporating data from more than 300,000 soil horizon samples. The test results show a poor performance of the models, all of which were found to be unsuitable for 31.1% of the textural triangle, accounting for 32.3% of the soil samples. Moreover, it is demonstrated that any conceivable model based solely on the broad particle‐size classes would suffer from a high degree of uncertainty. Consequently, the number of explanatory variables should be increased in order to improve the performance of models. An alternative prediction chart is provided for the first approximation of K‐factor, based on the textural triangle.

Generalized Ridge Regression Estimator in High Dimensional Sparse Regression Models

Modern statistical analysis often encounters linear models with the number of explanatory variables much larger than the sample size. Estimation in these high-dimensional problems needs some regularization methods to be employed due to rank deficiency of the design matrix. In this paper, the ridge estimators are considered and their restricted regression counterparts are proposed when the errors are dependent under a multicollinearity and high-dimensionality setting. The asymptotic distributions of the proposed estimators are exactly derived. Incorporating the information contained in the restricted estimator, a shrinkage type ridge estimator is also exhibited and its asymptotic risk is analyzed under some special cases. To evaluate the efficiency of the proposed estimators, a Monte-Carlo simulation along with a real example are considered.

Type 1 fuzzy function approach based on ridge regression for forecasting

Fuzzy function approach is a kind of fuzzy inference system that can produce successful results for the analysis of forecasting problems. In a fuzzy function approach, a fuzzy function corresponding to each fuzzy set is generated using multiple regression analysis. The number of explanatory variables in multiple regression analysis is increased via the non-linear transformations of the membership functions to improve the prediction performance of the model. In a fuzzy function approach, it can be found a high correlation between the non-linear transformations of membership functions, and therefore, the multiple linear regression method used to define fuzzy functions which has multicollinearity problem. The contribution of this paper is to propose a new fuzzy forecasting method to overcome this problem. In this paper, a new fuzzy function approach using ridge regression instead of multiple linear regression in Type 1 fuzzy function approach is proposed. The proposed new Type 1 approach is applied to various real world time series data and the results are compared to the ones obtained from other techniques. Thus, it is concluded that the results present superior forecasts performance.

Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models ( R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

MSE performance of the weighted average estimators consisting of shrinkage estimators when each individual regression coefficient is estimated

In this paper, we analytically derive the exact formula for the mean squared error (MSE) of two weighted average (WA) estimators for each individual regression coefficient. Further, we execute numerical evaluations to investigate small sample properties of the WA estimators, and compare the MSE performance of the WA estimators with the other shrinkage estimators and the usual OLS estimator. Our numerical results show that (1) the WA estimators have smaller MSE than the other shrinkage estimators and the OLS estimator over a wide region of parameter space; (2) the range where the relative MSE of the WA estimator is smaller than that of the OLS estimator gets narrower as the number of explanatory variables k increases.

The determinants of dividend policy in Euronext 100

The purpose of this paper is to examine the determinants of firms’ dividend policy, measured by dividend yield, using a sample of firms that belong to the Euronext 100 index for a period between 2007 and 2016. We used OLS regression with the dividend yield as the dependent variable and a number of explanatory variables at the firm level. Results show that the dividend yield in this paper is not associated with firms’ profitability, although both higher growth expectations by investors and larger size of firms negatively influence firms’ dividend yield. We found some evidence that leverage is indirectly related to more dividends. An important additional finding of this paper is that the level of leverage shapes dividend yields differently in the presence of stable payouts and stable dividends per share. Furthermore, the dividend yield reflects a positive valuation of investors if the growth in dividends is linked to the growth in earnings for firms with higher growth expectations, as a policy of a stable payout appears to be viewed by investors as not jeopardizing future growth. As dividend policy is a key part of Finance research, our study contributes to the theory twofold. First, by focusing on a specific niche not developed by literature, and second by examining the indirect effects of the traditional determinants of dividend policy.