Interval Regression Model Research Articles

The public perceptions and willingness to pay: from the perspective of the smog crisis in China

The deteriorating air quality, which is linked to human actions related to energy resources, has become an urgent global problem. China's air pollution has reached the hazardous level since 2000s. In consideration of the serious health and social problems, residential demand for clean air is increasing. This paper applies the Contingent Valuation method to estimate public's willingness to pay (WTP) for reducing air pollution in the urban area of China. Open-ended scenarios through face-to-face surveys are used to elicit public's WTP. The Two-Part model is employed for the estimation: the probit model is used to estimate the probability of respondents who have positive WTP values, and the interval regression model is employed to estimate the specific WTP level of those with nonzero WTP values. The results show that nearly 90% of the respondents are willing to pay for reducing air pollution, and the average amount of WTP per individual is 382.6RMB per year. Household income is found to be positively correlated with people's WTP, and those who trust the government are more willing to share the cost of reducing air pollution. This study recommends designing some effective mechanisms to tap the potentially available source of money for smog mitigation and improving the transparency of smog governance to enhance the government credibility as solutions to cope with smog crisis.

A cost–benefit analysis of improved irrigation when faced with the risks of climate change on Mount Kilimanjaro

This paper uses the contingent valuation method (CVM) to determine farmers׳ willingness to pay (WTP) for eliminating the risks of crop loss associated with climate change through access to improved irrigation. The data for the analysis was gathered by using a double-bounded survey of 225 randomly sampled farmers in 15 villages on Mount Kilimanjaro. To minimize initial value bias, respondents were randomly assigned initial values. The policy contribution consists of the valuation of improved irrigation in the presence of climate change risks, while the applied welfare contribution comprises empirical evidence about the impact of risk belief on welfare valuation. We argue on conceptual grounds that farmers׳ belief in risk is important, as it influences their valuation of the irrigation scheme ; however, risk belief׳s subjectivity makes it rather endogenous and therefore challenging to capture. Therefore, we use an Interval Regression Model with an endogenous explanatory variable to correct for the endogenous nature of the risk belief variable and account for the interval nature of the dependent variable. We found farmers׳ WTP to be between 7% and 21% of their income, and that the investment cost of improving an irrigation scheme could be paid back after a minimum of 13 years of operation.

Geometric parameters interval estimation of hydraulic valve by combining interval grey correlation analysis and ANFIS

Due to the effect of various factors in the processing, characteristics of hydraulic valve products and geometric parameters which affect the characteristics typically have a range of permitted tolerances, namely, with a certain uncertainty. It will be of great significance to study how to determine the range of geometric parameters of the product components in the design of the hydraulic valve product with specific performance requirements. In order to handle such interval data, this paper presents an interval regression model between the characteristics and geometric parameters of the hydraulic valve product by combining interval grey correlation analysis and ANFIS. Firstly, interval grey correlation analysis has been used to determine the main characteristics of hydraulic valve which affect a certain geometric parameter, and then the interval estimation model between the characteristics and geometric parameters of the hydraulic valve based on the main characteristics is established. The experimental results show that the model can perfectly fit the interval corresponding relationship between the product characteristics and geometric parameters.

A Zero‐Inflated Regression Model for Grouped Data

AbstractWe introduce the (panel) zero‐inflated interval regression (ZIIR) model, which is ideally suited when data are in the form of groups, and there is an ‘excess’ of zero observations. We apply our new modelling framework to the analysis of visits to the general practitioner (GP) using individual‐level data from the British Household Panel Survey. The ZIIR model simultaneously estimates the probability of visiting the GP and the frequency of visits (defined by given numerical intervals in the data). The results show that different socio‐economic factors influence the probability of visiting the GP and the frequency of visits.

China’s Conversion of Cropland to Forest Program for Household Delivery of Ecosystem Services: How Important is a Local Implementation Regime to Survival Rate Outcomes?

China’s Conversion of Cropland to Forests Program (CCFP) is the world’s largest afforestation-based Payments for Ecosystem Services (PES) program, having retired and afforested over 24 million ha involving 32 million rural households. Prior research has primarily focused on the CCFP’s rural welfare impacts, with few studies on program-induced environmental improvements, particularly at the household level. In this study, data from a 2010 survey covering 2808 rural households from across China was analyzed using an interval regression model to explain household-reported survival rates of trees planted on program-enrolled cropland. In addition to household-level factors, we explore the influence of local conditions and institutional configurations by exploiting the wide diversity of contexts covered by the data set. We find that households with more available labor and more forestry experience manage trees better, but that higher opportunity costs for both land and labor have the opposite effect. We also find that the local implementation regime- e.g., the degree of prior consultation with participants and regular monitoring - has a strong positive effect on reported survivorship. We suggest that the level of subsidy support to participating households will be key to survivorship of trees in planted CCFP forests for some time to come.

The normalized interval regression model with outlier detection and its real-world application to house pricing problems

Tanaka and his colleagues initially proposed fuzzy linear regression models in 1982. From then on, fuzzy regression analysis has been widely studied by many researchers. Since Tanaka's model uses the inclusion relationship between the given data and the estimated data, it was pointed out that Tanaka's model is sensitive to outliers. How to deal with the outlier problem has been a very important issue and attracted much attention from fuzzy regression researchers. Given crisp multiple independent variables and single interval dependent variable, we introduce the normalized upper and lower interval regression models and propose two outlier detection approaches for them. The dual problem of the normalized upper interval regression model is used to find out potential outliers. For the normalized lower interval regression model, the existence of the outliers makes it infeasible. The relaxation approach is proposed to find out the outliers. By deleting the outliers, we can find out the possibilistic functional relationship of the major data. A case study involving a house pricing problem is analyzed in detail. Household size, loan ratio and annual household income are identified as the independent variables and acceptable purchase price is the interval dependent variable. With the data collected from the survey in Shanghai, the normalized upper and lower interval regression models are built with deleting the outliers. The proposed models provide managerial insights into the real estate market and important policy implications in regulating urban land development.

Spatial issues on a hedonic estimation of rents in Brussels

Using Belgian microdata, we assess the impact, on a hedonic regression, of the distortions arising from the choice of either a specific zoning system or the delineation of the study area. We also evaluate the biases that arise when spatial effects are not accounted for. Given that the dependent variable is interval-coded, controlling for spatial dependence in this context is challenging. We address this problem with two alternative strategies. Firstly, we use the Gibbs Sampling algorithm to estimate spatial econometric models which extends the interval regression model. A major drawback of this approach is that the implied estimation is proned to the endogeneity biases inherent to our hedonic regression model. To circumvent the endogeneity issues triggered by the first estimation strategy, we also use a two-stage estimation procedure with locational fixed effects. In all specifications, results are sensitive to the Modifiable Areal Unit Problem (MAUP) and to the choice of the delineation of the study area. Moreover, they confirm the existence of substantive spatial dependence. Conversely to the previous results with a negative elasticity for the percentage of the area covered by agriculture and a positive elasticity for the potential accessibility to jobs, the second approach implies opposite effects for those two variables. This indicates that dwellings close to agricultural areas and with a lower accessibility to the main employment centers are highly demanded and that endogeneity biases are not negligible.

Intergenerational Transfers and Asset Inequality in Japan: Empirical Evidence from New Survey Data

This paper quantitatively examines the impact of intergenerational transfers on asset inequality among Japanese households. We estimate an intergenerational asset transfer function with various control variables, using a unique micro dataset taken from a survey conducted by the Economic and Social Research Institute, Cabinet Office. Employing three different models, a Tobit model, an interval regression model and an ordered probit model, to ensure that our results are independent of the specific econometric approach used, we investigate whether asset transfers received are correlated with households’ financial strength. We find that higher income households are likely to receive larger asset transfers. However, the contribution of intergenerational transfers to asset inequality appears to be small.

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Interval Regression Model Using L(p,q) -Estimator

In this paper we introduce an interval regression model, having interval coefficients and crisp input data, and propose a L(p,q)-estimator for the interval coefficients. The L(p,q)-estimator uses one Lp-norm to estimate the left end point of the interval coefficients and independently another Lq-norm for the width of the intervals. An example is presented showing how the results of the regression model vary as we change p and q which implies we need to find the “best p and q. An error measure is defined and we use response surface methodology to search for the optimal values for p and q to minimize this error.

A Novel Sensor Fault Detection Method

To detect the sensor faults of the plant with min-disturbance, a weighted least squares interval regression model is proposed. The output of the proposed model is an interval band which can resist the disturbance influence and give correct sensor fault alarm. Additionally, the time complexity of this model is low because only a set of linear equations can determine the parameters. The experiments of fault instance demonstrate the feasibility and effectiveness of the interval regression model. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3684

Smokers’ Sexual Behavior and Their Satisfaction with Family Life

Smokers tend to be more impatient and prefer immediate benefits compared with non-smokers. If people follow their primitive instincts they will engage in sexual behavior. Impatient people are more likely to have sex. However, for married people, having sex with a spouse is considered to be an investment in family life. Based on individual level data of Japan General Social Surveys, this paper investigated how sexual behavior differs between smokers and non-smokers. In addition to it, determinants of life satisfaction are also investigated. The important findings through the interval regression model and the ordered probit model are: (1) frequency of sex is positively associated with family satisfaction; (2) unmarried smokers are more likely to have sex than unmarried non-smokers; (3) married smokers are less likely to have sex than married non-smokers. It follows from estimation results that smoking behavior alerts us to his unobserved characteristics before marriage, thus reducing information asymmetry.

Public Acceptance and Willingness-to-Pay for a Future Dengue Vaccine: A Community-Based Survey in Bandung, Indonesia

BackgroundAll four serotypes of dengue virus are endemic in Indonesia, where the population at risk for infection exceeds 200 million people. Despite continuous control efforts that were initiated more than four decades ago, Indonesia still suffers from multi-annual cycles of dengue outbreak and dengue remains as a major public health problem. Dengue vaccines have been viewed as a promising solution for controlling dengue in Indonesia, but thus far its potential acceptability has not been assessed.Methodology/Principal FindingsWe conducted a household survey in the city of Bandung, Indonesia by administering a questionnaire to examine (i) acceptance of a hypothetical pediatric dengue vaccine; (ii) participant's willingness-to-pay (WTP) for the vaccine, had it not been provided for free; and (iii) whether people think vector control would be unnecessary if the vaccine was available. A proportional odds model and an interval regression model were employed to identify determinants of acceptance and WTP, respectively. We demonstrated that out of 500 heads of household being interviewed, 94.2% would agree to vaccinate their children with the vaccine. Of all participants, 94.6% were willing to pay for the vaccine with a median WTP of US$1.94. In addition, 7.2% stated that vector control would not be necessary had there been a dengue vaccination program.Conclusions/SignificanceOur results suggest that future dengue vaccines can have a very high uptake even when delivered through the private market. This, however, can be influenced by vaccine characteristics and price. In addition, reduction in community vector control efforts may be observed following vaccine introduction but its potential impact in the transmission of dengue and other vector-borne diseases requires further study.

Exceeding the speed limit: prevalence and determinants in Iran

Speeding is one of the most common risk behaviours associated with crashes causing signficant injury. The objective of this study is to explore the prevalence and determinants of speeding on a road between Tehran and Hamadan, Iran. In a cross-sectional study in 2009, stretches of the road were studied including three groups of posted speed limits: < 50 km/h, 50–100 km/h and > 100 km/h. Each stretch was evaluated both in daylight and dark. Randomly identified driver's speed was checked by a handheld speed camera and then the driver was invited to participate in a survey. Statistical analysis was performed using Chi-Square, crude and adjusted odds ratio, 95% confidence interval and multiple logistic regression models. Overall, 52.8% of the drivers were travelling more than 10 km/h above the posted limit. Where limits were < 50 km/h, 74.6% of drivers were speeding. This declined to 46.9% for sections with limits between 50 and 100 km/h and to 36.9% for sections posted more than 100 km/h. Finally, more than half the drivers were observed to be speeding. Driving more than the posted limit was far more likely on the areas with the lowest posted speed limits, personal passenger vehicles, modern vehicles not using seat belts, and male drivers.

HIV shedding in the oral cavity: an assessment of HIV type, immunovirologic, demographic and oral factors

ObjectiveTo quantify the prevalence and burden of HIV type 2 (HIV-2) and HIV-1 RNA in the oral cavity of antiretroviral therapy-naive HIV-infected Senegalese individuals and to identify correlates of oral...

Willingness to Pay for Adverse Drug Event Regulatory Actions

Regulatory requirements for the pharmaceutical industry have become increasingly demanding with respect to the safety and effectiveness of drugs. The objective of this study was to determine the willingness to pay (WTP), of both the Dutch general public and dialysis patients, for regulatory requirements related to reducing the risk of pure red cell aplasia (PRCA) associated with epoetin alpha use. A survey was carried out in April 2009. The Dutch general public (n = 422) was approached through a survey sampling agency. Patients (n = 112) were included through several Dutch dialysis clinics because they are often treated with epoetin alpha and therefore were expected to have a higher WTP than the general public. The survey aimed to determine the WTP for reducing the risk of PRCA in epoetin alpha users from 4.5 to 0 per 10 000 patients per year, based on regulatory actions that have been taken by the European Medicines Agency (EMA). WTP was determined via a payment scale and an open-ended follow-up question. Patients were asked how much extra per year they would be willing to pay for their basic healthcare insurance. We used two censored regression models to test the association between WTP and a set of independent variables: a Tobit model with the stated WTP as the dependent variable and an interval regression model with the interval between the lower and upper bounds of the payment scale as the dependent variable. The patients' mean WTP was significantly higher (€46.52) than that of the general public (€24.40). The Tobit model showed significant associations (α = 0.05) with WTP for dialysis patients, risk perception and respondents' opinions on costs of healthcare. The interval regression model showed significant associations with WTP for the same variables as the Tobit model and for one additional variable (risk aversion). Income did not significantly affect WTP. A scenario with a 10-fold larger risk reduction did not increase WTP significantly. This study is, as far as we know, one of the first attempts to analyse the WTP for drug regulation and should in future be used in studies of the societal costs of drug regulation for epoetin alpha use. Our results indicate that the Dutch general public, especially Dutch dialysis patients, are willing to pay limited amounts to reduce the risk of serious adverse events associated with drug use.

Interval regression models with endogenous explanatory variables

We consider the estimation of linear models where the dependent variable is observed by intervals and some continuous regressors may be endogenous. Our approach, an IV version of the technique devised by Stewart (Rev Econ Stud 50(3):737–753, 1983), is fully parametric and two estimators are proposed: a two-step estimator and a limited-information maximum-likelihood estimator. The results can be summarized as follows: the two-step estimator has an intuitive appeal, and a Monte Carlo experiment suggests that its relative efficiency is rather satisfactory. The limited-information maximum-likelihood estimator, however, is probably simpler to implement and has the advantage of providing a framework in which several testing procedures are more straightforward to perform. The application of two-stage least squares to a proxy of the dependent variable built by taking midpoints, on the other hand, leads to inconsistent estimates; Monte Carlo evidence suggests that the bias arising from the “midpoint” technique is much worse than the effect of distributional misspecification. An example application is also included, which uses Australian data on migrants’ remittances; endogeneity effects are substantial and using conventional estimation methods leads to substantially misleading inference.

Fitting Fully Observed Recursive Mixed-process Models with cmp

At the heart of many econometric models are a linear function and a normal error. Examples include the classical small-sample linear regression model and the probit, ordered probit, multinomial probit, tobit, interval regression, and truncated-distribution regression models. Because the normal distribution has a natural multidimensional generalization, such models can be combined into mul-tiequation systems in which the errors share a multivariate normal distribution. The literature has historically focused on multistage procedures for fitting mixed models, which are more efficient computationally, if less so statistically, than maximum likelihood. Direct maximum likelihood estimation has been made more practical by faster computers and simulated likelihood methods for estimating higher-dimensional cumulative normal distributions. Such simulated likelihood methods include the Geweke–Hajivassiliou-Keane algorithm (Geweke, 1989, Econometrica 57: 1317–1339; Hajivassiliou and McFadden, 1998, Econometrica 66: 863–896; Keane, 1994, Econometrica 62: 95–116). Maximum likelihood also facilitates a generalization to switching, selection, and other models in which the number and types of equations vary by observation. The Stata command cmp fits seemingly unrelated regressions models of this broad family. Its estimator is also consistent for recursive systems in which all endogenous variables appear on the right-hand sides as observed. If all the equations are structural, then estimation is full-information maximum likelihood. If only the final stage or stages are structural, then estimation is limited-information maximum likelihood. cmp can mimic a score of built-in and user-written Stata commands. It is also appropriate for a panoply of models that previously were hard to estimate. Heteroskedasticity, however, can render cmp inconsistent. This article explains the theory and implementation of cmp and of a related Mata function, ghk2(), that implements the Geweke–Hajivassiliou–Keane algorithm.

A Parameter Selection Method for Support Vector Interval Regression Model

The support vector interval regression model is an effective method to estimate imprecise data. Parameters of this model is very important in order to obtain the excellent regression result. The flexible polyhedron search algorithm is a fast optimization algorithm. Based on the flexible polyhedron search algorithm, this paper proposes an automatic parameters selection method for the support vector interval regression model. Experiments illustrate the validity and applicability of the support vector interval regression model based on the flexible polyhedron search algorithm.

Expenditures on Sport Apparel: Creating Consumer Profiles through Interval Regression Modelling

Using a heterodox theoretical approach, this article presents sport consumer profiles based on socio-demographic and sport-related lifestyle characteristics. Sport apparel is operationalized as a categorical, hierarchical variable. Given the censored nature of the dependent variable, a two step Heckman-type approach with an interval regression model was used. Data were obtained from a cross-sectional sample of adults in Flanders, Belgium (N=1355). The results indicate that the decision to spend money on sport clothing and shoes is mainly determined by sport-related lifestyle characteristics, confirming the emerging importance of lifestyle in understanding the decision to consume material goods. However, the variability in the amount of money spent on sport apparel is explained by both socio-demographic variables and sport-related lifestyle characteristics. Consequently, both socio-demographic and lifestyle variables are used in the interval regression models, which is introduced as a novel technique to create consumer profiles. These profiles assist sporting goods marketers in refining their strategies to reach specific target markets.