Journal articles: 'Tobit regression model'

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Kadhim Abbas, Haider. "Bayesian Lasso Tobit regression." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (August 26, 2019): 1–13. http://dx.doi.org/10.29304/jqcm.2019.11.2.553.

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In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

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Lee, Seung-Chun, and Byung Su Choi. "Bayesian Interval Estimation of Tobit Regression Model." Korean Journal of Applied Statistics 26, no. 5 (October 31, 2013): 737–46. http://dx.doi.org/10.5351/kjas.2013.26.5.737.

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Li, Lexin, Jeffrey S. Simonoff, and Chih-Ling Tsai. "Tobit model estimation and sliced inverse regression." Statistical Modelling: An International Journal 7, no. 2 (July 2007): 107–23. http://dx.doi.org/10.1177/1471082x0700700201.

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Flaih, Ahmad, Jose Guardiola, Hassan Elsalloukh, and Chary Akmyradov. "Statistical Inference on the ESEP Tobit Regression Model." Journal of Statistics Applications & Probability Letters 6, no. 1 (January 1, 2019): 1–9. http://dx.doi.org/10.18576/jsapl/060101.

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Song, Weixing. "Distribution-free test in Tobit mean regression model." Journal of Statistical Planning and Inference 141, no. 8 (August 2011): 2891–901. http://dx.doi.org/10.1016/j.jspi.2011.03.012.

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Ding, Hao, Zhanfeng Wang, and Yaohua Wu. "Tobit regression model with parameters of increasing dimensions." Statistics & Probability Letters 120 (January 2017): 1–7. http://dx.doi.org/10.1016/j.spl.2016.09.006.

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Koul, Hira L., Weixing Song, and Shan Liu. "Model checking in Tobit regression via nonparametric smoothing." Journal of Multivariate Analysis 125 (March 2014): 36–49. http://dx.doi.org/10.1016/j.jmva.2013.11.017.

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Chib, Siddhartha. "Bayes inference in the Tobit censored regression model." Journal of Econometrics 51, no. 1-2 (January 1992): 79–99. http://dx.doi.org/10.1016/0304-4076(92)90030-u.

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Yu, Keming, and Julian Stander. "Bayesian analysis of a Tobit quantile regression model." Journal of Econometrics 137, no. 1 (March 2007): 260–76. http://dx.doi.org/10.1016/j.jeconom.2005.10.002.

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鲁, 海波. "A Sequential Shrinkage Estimate Based on Tobit Regression Model." Pure Mathematics 11, no. 07 (2021): 1320–25. http://dx.doi.org/10.12677/pm.2021.117148.

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Lu, Haibo, Cuiling Dong, and Juling Zhou. "A Sequential Shrinkage Estimating Method for Tobit Regression Model." Open Journal of Modelling and Simulation 09, no. 03 (2021): 275–80. http://dx.doi.org/10.4236/ojmsi.2021.93018.

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Prahutama, Alan, Agus Rusgiyono, Sudarno, Moch Abdul Mukid, and Tatik Widiharih. "Analysis of Household Expenditures on Education in Semarang City, Indonesia Using Tobit Regression Model." E3S Web of Conferences 125 (2019): 09016. http://dx.doi.org/10.1051/e3sconf/201912509016.

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Data on household expenditure for education is censored data. One method used to analyze this censored data is Tobit regression. Tobit regression changes the response variable which was originally a nominal scale into a mixed scale measurement. Tobit regression can overcome the non-biased categorical regression model. This study aims to analyze the factors that influence household expenditure on education in Semarang city. The dependent variable used in this study is household expenditure for education. The independent variables used include the Education of the Head of the Household, Occupation of the Head of the household, number of household members, Number of Working Household Members, the proportion of household members who attend school ≤ in Junior High School, Senior High School and College, food expenditure in households and regions. Based on the Tobit regression model obtained, the factors that influence household expenditure for education include the number of household members (ART), Number of Working Household Members, the proportion of household members who attend school ≤ in Junior High School, Senior High School and College, food expenditure in households. Generate market research especially in customer satisfaction.

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Terza, Joseph V. "A Tobit-type estimator for the censored Poisson regression model." Economics Letters 18, no. 4 (January 1985): 361–65. http://dx.doi.org/10.1016/0165-1765(85)90053-9.

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Lee, Byung-Joo. "A nested Tobit analysis for a sequentially censored regression model." Economics Letters 38, no. 3 (March 1992): 269–73. http://dx.doi.org/10.1016/0165-1765(92)90069-b.

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Andiappan, M., FJ Hughes, S. Dunne, W. Gao, and ANA Donaldson. "Adjusting the Oral Health Related Quality of Life Measure (Using Ohip-14) for Floor and Ceiling Effects." Journal of Oral Health and Community Dentistry 9, no. 3 (2015): 99–104. http://dx.doi.org/10.5005/johcd-9-3-99.

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ABSTRACT Introduction The influence of floor (lowest) and ceiling (highest) effects on the outcome measure is of serious concern particularly when the outcome is measured using Likert scales. Conventional regression methods yield biased results and hence tobit regression is to be used to adjust for these effects. This paper is an attempt to use tobit regression in finding the predictors of oral health related quality of life after adjusting for floor and ceiling effects. Methods A sample of 360 participants were asked to self asses their oral health related quality of life (OHRQoL) using Oral Health impact profile with 14 items which forms the data for this study. Apart from descriptive statistics, Ordinary Least squares regression and tobit regression were used to find the significant predictors of OHRQoL and the results of both methods were compared. Results The sample comprised of 41.1% men and 58.9% women. Majority of the participants (68.3%) were whites. The average item difficulty was 0.4 and the average item easiness was 0.03. The floor and ceiling values for the composite scores were 14 and 56 respectively. Age and gender were not statistically significant both in Ordinary Least Squares (OLS) regression and Tobit regression. Full time employment, student and retired have significantly lower scores in OLS but only retired had significantly lower scores in the tobit model. Conclusion Tobit model, after adjusting for floor and ceiling effect, gives higher values for the predictors and the OLS model underestimates the effects of predictors on OHIP scores.

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Geng, Pei, and Hira L. Koul. "Model checking in Tobit regression with measurement errors using validation data." Journal of Statistical Planning and Inference 190 (November 2017): 15–31. http://dx.doi.org/10.1016/j.jspi.2017.05.002.

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Yue, Yu Ryan, and Hyokyoung Grace Hong. "Bayesian Tobit quantile regression model for medical expenditure panel survey data." Statistical Modelling: An International Journal 12, no. 4 (August 2012): 323–46. http://dx.doi.org/10.1177/1471082x1201200402.

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Wang, Wei, and Michael E. Griswold. "Natural interpretations in Tobit regression models using marginal estimation methods." Statistical Methods in Medical Research 26, no. 6 (September 1, 2015): 2622–32. http://dx.doi.org/10.1177/0962280215602716.

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The Tobit model, also known as a censored regression model to account for left- and/or right-censoring in the dependent variable, has been used in many areas of applications, including dental health, medical research and economics. The reported Tobit model coefficient allows estimation and inference of an exposure effect on the latent dependent variable. However, this model does not directly provide overall exposure effects estimation on the original outcome scale. We propose a direct-marginalization approach using a reparameterized link function to model exposure and covariate effects directly on the truncated dependent variable mean. We also discuss an alternative average-predicted-value, post-estimation approach which uses model-predicted values for each person in a designated reference group under different exposure statuses to estimate covariate-adjusted overall exposure effects. Simulation studies were conducted to show the unbiasedness and robustness properties for both approaches under various scenarios. Robustness appears to diminish when covariates with substantial effects are imbalanced between exposure groups; we outline an approach for model choice based on information criterion fit statistics. The methods are applied to the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort study to assess associations between obesity and cognitive function in the non-Hispanic white participants.

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Xu, Xuecai, and Željko Šarić. "Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach." Journal of Advanced Transportation 2018 (August 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/5032497.

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This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

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Anderson, Jason, and Salvador Hernandez. "Heavy-Vehicle Crash Rate Analysis: Comparison of Heterogeneity Methods Using Idaho Crash Data." Transportation Research Record: Journal of the Transportation Research Board 2637, no. 1 (January 2017): 56–66. http://dx.doi.org/10.3141/2637-07.

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Studies investigating crash rates by roadway classification are few and far between and even more rare if extended to focus on heavy vehicles. This study explored and compared two advanced econometric methods—random-parameter Tobit regression and latent class Tobit regression—to determine contributing factors for heavy-vehicle crashes per million vehicle miles traveled while accounting for the unobserved heterogeneity present in crash data. The increasing crash rates in Idaho, crash proportion by roadway classification, and available data made an ideal case study. Empirical results show that although the random-parameter Tobit regression model provides better insight into heavy-vehicle crash rates than the fixed-parameter approach, the latent class Tobit regression model is the preferred methodology for the given data set. Traffic volumes, roadway characteristics, and traffic control devices were among the variables found to be statistically significant. Results from this study provide an alternate framework to account for heterogeneity while identifying key factors by roadway classification that influence heavy-vehicle crash rates. The illustrated framework and analysis by roadway classification can provide guidance to transportation agencies and policy makers and prompt future studies to include a latent class analysis, analysis by road classification, or both.

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Gandhi, Aradhana, and Ravi Shankar. "Efficiency measurement of Indian retailers using Data Envelopment Analysis." International Journal of Retail & Distribution Management 42, no. 6 (June 3, 2014): 500–520. http://dx.doi.org/10.1108/ijrdm-10-2012-0094.

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Purpose – The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area. Design/methodology/approach – This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression. Findings – DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India. Research limitations/implications – The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression. Originality/value – This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.

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Wang, Lan. "A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model." Statistics & Probability Letters 77, no. 10 (June 2007): 1034–42. http://dx.doi.org/10.1016/j.spl.2007.01.017.

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Ji, Yonggang, Nan Lin, and Baoxue Zhang. "Model selection in binary and tobit quantile regression using the Gibbs sampler." Computational Statistics & Data Analysis 56, no. 4 (April 2012): 827–39. http://dx.doi.org/10.1016/j.csda.2011.10.003.

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البياتي, محمود مهدي, and هيثم حسون ماجد. "نموذج Tobit Quantile Regression البيزي باستعمال المستوى الرابع من التوزيعات الأولية." Journal of Economics and Administrative Sciences 24, no. 105 (October 20, 2018): 487. http://dx.doi.org/10.33095/jeas.v24i105.59.

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الملخص تم في هذا البحث مناقشة تقدير معلمات واختيار المتغيرات في نموذج (Tobit Quantile Regression) في ظل وجود مشكلة التعدد الخطي. ولأهمية تقنية (elastic net) في التعامل مع مشكلة التعدد الخطي واختيار المتغيرات في نماذج الانحدار فقد تم اعتماد هذه التقنية في الدراسة . تم في هذه الدراسة اقتراح النموذج الهرمي البيزي (Bayesian Tobit hierarchical model) في المستوى الرابع من التوزيعات الاولية بالاعتماد على البيانات وبافتراض ان كلا معلمتي التنظيم (tuning parameter) في تقنية elastic net هي متغيرات عشوائية ويتم تقديرها مع بقية المعلمات في النموذج . تم استخدام اسلوب المحاكاة في بيان كفاءة الطريقة المقترحة كما تم مقارنة الطريقة المقترحة مع بعض الطرائق الحديثة المستخدمة في تقدير معلمات نموذج (Tobit Quantile regression)واظهرت النتائج الاداء الافضل للطريقة المقترحة . هذا هو العمل الاول (بحسب علم الباحث) الذي يتم فيه مناقشة تقدير واختيار المتغيرات لنموذج TQR باقتراح النموذج الهرمي البيزي في المستوى الرابع من التوزيعات الاولية وبافتراض ان كلا معلمتي التنظيم في تقنية elastic net هي متغيرات عشوائية .

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Park, Minho, and Dongmin Lee. "Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model." Mathematical Problems in Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/7273630.

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In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

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Lee, Seung-Chun, and Byongsu Choi. "A Comparison of Bayesian and Maximum Likelihood Estimations in a SUR Tobit Regression Model." Korean Journal of Applied Statistics 27, no. 6 (December 31, 2014): 991–1002. http://dx.doi.org/10.5351/kjas.2014.27.6.991.

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Lee, Sang Hyuk, and Jung-Beom Lee. "An Analysis on Vehicle Accident Factors of Intersections using Random Effects Tobit Regression Model." Journal of The Korea Institute of Intelligent Transport Systems 16, no. 1 (February 28, 2017): 26–37. http://dx.doi.org/10.12815/kits.2017.16.1.26.

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Miao, Jing Yi, and Cheng Wei Cai. "The Study on the Technical Efficiency of the Listed Coal Companies." Advanced Materials Research 616-618 (December 2012): 528–32. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.528.

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This paper analyses China's listed coal companies based on Tobit-DEA model. DEA model results show that these companies have low technical efficiency level, containing the high scale efficiency and low pure technical efficiency. Then dig the factors to the pure technical efficiency and scale efficiency with Tobit regression model, and the result shows the increasing of management fee rate and debt capital ratio of technical efficiency has some negative effects, and the increasing of sales rate, asset turnover and P/E ratio is beneficial to technical efficiency. Then analyzes the possible reasons for these results and gives suggestions.

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Guo, Hainan, Yang Zhao, Tie Niu, and Kwok-Leung Tsui. "Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model." PLOS ONE 12, no. 9 (September 8, 2017): e0184211. http://dx.doi.org/10.1371/journal.pone.0184211.

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Krupa, Thomas, Kirils Farbarzevics, and Bassam Salame. "INVESTMENT EFFICIENCY OF LIFE INSURANCE COMPANIES IN GERMANY: APPLICATION OF A TWO-STAGE SBM." Współczesna Gospodarka 10, no. 1 (32) (March 31, 2019): 79–91. http://dx.doi.org/10.26881/wg.2019.1.08.

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Purpose – To prove the robustness of the efficiency-measuring model against potentially system-relevant disturbances to company variables such as SIZE, ROA, solvency and organizational form. Methodology – In the first stage, the established model is applied using the SBM to measure insurance efficiency. The underlying data sets are from the twenty biggest life insurance companies (2008-2017) in Germany. In the second stage, the established model is examined for its robustness against disturbance variables. Several disturbance variables are introduced individually to the system and examined for their influence by three econometric methods, Tobit regression, OLS and the fixed-effect model. This approach allows a comparative analysis of the results with respect to the systemic relevance of every added variable. In the end, the accuracy of the second stage is compared through the Spearman test. Findings – The comparative analysis of all three econometric techniques brought ROA as an efficiency-influencing variable. Furthermore, both proved econometric models Tobit and OLS are SBM-suitable with cross-sectional data. Further evidence for SBM compatibility are found for Tobit and the fixed-effect model with panel data. JEL classification: C510, C520

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Pérez-Reyes, Raúl, and Beatriz Tovar. "Peruvian Electrical Distribution Firms’ Efficiency Revisited: A Two-Stage Data Envelopment Analysis." Sustainability 13, no. 18 (September 8, 2021): 10066. http://dx.doi.org/10.3390/su131810066.

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The extent to which the structural reform of the Peruvian electricity market in the 1990s has improved the technical efficiency levels of the distribution companies and whether some firm specific explanatory variables had influenced upon the efficiency was analysed for first time using a second stage Tobit model to study the influence of some firm specific explanatory variables on efficiency. Some authors have argued that the use of Tobit regression is inappropriate in the second stage of DEA and have suggested using other recently developed options. Due to this, it might be worth revisiting this issue and adding those other alternative models to check whether the conclusions obtained with the Tobit model could be upheld. The nine alternative models estimated allow us to confirm that the incentives generated by the reform process led to the firms becoming more efficient. Moreover, private management and the ratio of low voltage sales to medium voltage sales for each company positively affect efficiency, whereas investment per customer is negatively correlated to it.

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Pullenayegum, Eleanor M., Jean-Eric Tarride, Feng Xie, and Daria O’Reilly. "Calculating Utility Decrements Associated With an Adverse Event." Medical Decision Making 31, no. 6 (January 6, 2011): 790–99. http://dx.doi.org/10.1177/0272989x10393284.

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Background: When calculating the decreases in health utility associated with adverse events, often a number ofrespondents achieve the upper utility bound of 1. “Marginal” Tobit or CLAD coefficients have been used to account for this. These are calculated by using a Tobit or a CLAD model to estimate the decrease in a latent unbounded variable associated with the event or condition, then to multiply by the proportion of respondents falling below 1 in order to transform back to the utility scale. Objective & Methods: Starting with the Tobit model, we show mathematically that this procedure is not valid, when calculating decreases in utility associated with binary events. We then generalize the result to the CLAD model. A selection of published studies is used to illustrate the bias in the marginal Tobit decrements. Results: The degree of bias is more severe the greater the decrease in utility associated with the event, and the larger the proportion of individuals at the upper ceiling.In the examples studied, the degree of bias was often greater than 10%. We provide the correct formula for calculating the utility decrement. Conclusions: The marginal Tobit and CLAD coefficients should not be used as estimates of a utility decrement corresponding to an adverse event or health condition unless the coefficients are small in absolute value, or if the proportion of individuals at the upper utility bound is small. In other settings, the corrected formula or alternative regression methods (e.g. linear models of mean utility) should be considered.

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White, Paul, and Pat Sessford Pat Sessford. "A TOBIT REGRESSION MODEL OF FOOTBALL PERFORMANCE VARIABLES FOR A PROFITABLE FIXED ODDS BETTING STRATEGY." Journal of Gambling Business and Economics 4, no. 3 (January 2, 2013): 1–12. http://dx.doi.org/10.5750/jgbe.v4i3.558.

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An analysis of in-match football team performances in European top flight matches has been undertaken to develop profitable betting strategies in the fixed odds market. Three profitable betting strategies for betting on the home win are derived using a novel application of the Tobit regression model. Net profits of in excess of 15% are obtained in unseen out-of-sample testing. The generation of these profits may indicate that general team performance information may not be correctly factored into the fixed odds offered by bookmakers.

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Chen, Yuxiu, Long Li, Jian Yu, Sang-Bing Tsai, and Jiangtao Wang. "An Empirical Study on Environmental Efficiency Measurements and Influencing Factors." Ecological Chemistry and Engineering S 27, no. 4 (December 1, 2020): 543–53. http://dx.doi.org/10.2478/eces-2020-0033.

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Abstract Airlines are an important part of the comprehensive transportation system. Therefore, it is of great practical significance to empirically analyse the measurement of airlines’ environmental efficiency and influencing factors. Adopting the SBM-DEA model, this study measured the environmental efficiency of 20 Chinese airlines between 2010 and 2017 and empirically analysed the factors influencing their environmental efficiency using a Tobit regression model. Spring Airlines, China Southern Airlines, and Hainan Airlines were found to have the highest environmental efficiency. Tianjin Airlines, Hebei Airlines, and China Express Airlines had the lowest environmental efficiency. The Tobit regression results showed that average flight distance, load factor, market share, and proportion of cargo and mail turnover had a significant positive effect on airline environmental efficiency. Fuel consumption per ton-km had a significant negative effect on airline environmental efficiency.

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Liu, Lixia, and Xueli Zhan. "Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning." Complexity 2019 (July 10, 2019): 1–11. http://dx.doi.org/10.1155/2019/9190273.

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Agricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises’ financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises.

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JERYANA, I. PUTU, I. PUTU EKA NILA KENCANA, and G. K. GANDHIADI. "MODEL REGRESI TOBIT KONSUMSI SUSU CAIR PABRIK (Studi Kasus Rumah Tangga di Provinsi Bali)." E-Jurnal Matematika 3, no. 2 (May 31, 2014): 75. http://dx.doi.org/10.24843/mtk.2014.v03.i02.p068.

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Regression analysis is used to study the relationship between dependent (response) variable with one or more independent (causal) variables. While response data were censored, then Tobit regression model could be applied. According to Greene (2003), censored data were data with incomplete observation or the dependent variable has a value of zero, while for the other observations have particular value. This research aimed to model dairy milk’s consumption from households at Bali Province. By using data from Survey SosialEkonomiNasional (SUSENAS) or Social Economy’s National Survey (SENS) for year 2012, 615 households were selected as sampling unit using simple random sampling technique, and found 123 households who consumed dairy milk. The independent variables in our model were last education level completed by head of household’s (X1), head of household’s work (X2), age of head of household’s (X3), amount of expenditure for food consumption’s (X4), number of household members (X5), and household income (X6), the response variable was budget for buying dairy milk (Y). From six independent variables, is found only last education level by head household and amount of expenditure for food consumption had siginficant effect on Y’s. The final Tobit regression model were obtained using AIC (Akaike Information Criterion) method is Y = -3314724 + 565429,7 X1 + 0,014278 X4 with pseudo R2 as much as 16.79 per cent.

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Al-Assa'd, Tamer, and Johannes Sauer. "The performance of water utilities in Jordan." Water Science and Technology 62, no. 4 (August 1, 2010): 803–8. http://dx.doi.org/10.2166/wst.2010.907.

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This paper aims to measure the relative performance of Jordan's water utilities for water sector (WS) and wastewater sector (WWS), and to investigate the major factors behind their inefficiencies. DEA (non-parametric method) and Tobit model (parametric method) were used at two stages. At the first stage DEA was used to evaluate the utilities' efficiency and to investigate the utilities' scale effect on efficiency. At the second stage Tobit model (regression) is applied to determine the impact of the non-controllable factors on utilities' inefficiencies where the efficiency score is the dependent variable in the regression. The results of DEA showed that there is a potential to reduce inputs level: water supply and operational expense (OPEX) for WS, and treated wastewater and OPEX for WWS, by around 15–20 and 23–27%, respectively. Aqaba and Jarash utilities are the most efficient utilities in the WS, while it is Amman utility in the WWS. The utility size has relatively moderate effect on the relative performance. It is concluded that the medium utility size for WS and large utility size for WWS is the most appropriate. Tobit model results indicate that commercialization activities have a clear positive impact on efficiency improvement for both sectors.

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Chesher, Andrew, and Simon Peters. "Symmetry, Regression Design, and Sampling Distributions." Econometric Theory 10, no. 1 (March 1994): 116–29. http://dx.doi.org/10.1017/s0266466600008252.

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When values of regressors are symmetrically disposed, many M-estimators in a wide class of models have a reflection property, namely, that as the signs of the coefficients on regressors are reversed, their estimators' sampling distribution is reflected about the origin. When the coefficients are zero, sign reversal can have no effect. So in this case, the sampling distribution of regression coefficient estimators is symmetric about zero, the estimators are median unbiased and, when moments exist, the estimators are exactly uncorrelated with estimators of other parameters. The result is unusual in that it does not require response variates to have symmetric conditional distributions. It demonstrates the potential importance of covariate design in determining the distributions of estimators, and it is useful in designing and interpreting Monte Carlo experiments. The result is illustrated by a Monte Carlo experiment in which maximum likelihood and symmetrically censored least-squares estimators are calculated for small samples from a censored normal linear regression, Tobit, model.

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Guo, Hainan, Yang Zhao, Tie Niu, and Kwok-Leung Tsui. "Correction: Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model." PLOS ONE 13, no. 2 (February 15, 2018): e0193266. http://dx.doi.org/10.1371/journal.pone.0193266.

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Nistor, Cristina Silvia, Cristina Alexandrina Ștefănescu, and Andrei-Răzvan Crișan. "Performance Through Efficiency in the Public Healthcare System – A DEA Approach in an Emergent Country." Studia Universitatis Babes-Bolyai Oeconomica 62, no. 1 (April 25, 2017): 31–49. http://dx.doi.org/10.1515/subboec-2017-0003.

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AbstractAiming to analyze the efficiency of the public sector, this paper enriches the literature by providing insights of the healthcare system for an emergent country - Romania. The empirical findings reached by applying the input orientated-variable return to scale (VRS) model of Data Envelopment Analysis (DEA) and Tobit regression method are determined on two key levels of the study. The Data Envelopment Analysis technique quantifies the efficiency within 20 representative hospitals located in the four administrative macro-regions, highlighting the ways of increasing efficiency, while the Tobit regression identifies the factors that influence the efficiency level. The results of the investigation allow for comparisons with other emerging countries, as efficiency has become an increasingly significant factor for public sector evolution.

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CHEN, CHING-LUNG, and CHEI-WEI WU. "DIAGNOSING ASSETS IMPAIRMENT BY USING RANDOM FORESTS MODEL." International Journal of Information Technology & Decision Making 11, no. 01 (January 2012): 77–102. http://dx.doi.org/10.1142/s0219622012500046.

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This study develops a diagnosing model to examine the outcomes of assets write-off in enriching the literatures of assets impairment. Prior studies employed the Logit, linear and Tobit regression models to classify the determination of assets impairment and to diagnose the magnitude of the impairment, respectively. However, the drivers of assets write-off are somewhat complicated explicitly or implicitly, these models are unlikely to provide fairly satisfactory results. To improve the diagnosis, the Random Forests model is used for the classification determining and the magnitude diagnosing of assets impairment in this study. The result reveals that the Random Forests model outperforms the Logit and linear regression models in each case with variables selected by individual wrapping approach. This study also demonstrates diagnostic checks for both models with similar selected variables. The results are robust to these various specifications.

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Gandhi, Aradhana Vikas, and Dipasha Sharma. "Technical efficiency of private sector hospitals in India using data envelopment analysis." Benchmarking: An International Journal 25, no. 9 (November 29, 2018): 3570–91. http://dx.doi.org/10.1108/bij-06-2017-0135.

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Purpose The purpose of this paper is to ascertain the performance of Indian hospitals in recent past and derive meaningful insights for policy makers and practicing managers in this area. Design/methodology/approach This paper analyses the technical efficiency of select Indian private hospitals using three related methodologies: data envelopment analysis (DEA), Malmquist Productivity Index (MPI) and Tobit regression. Two output variables (i.e. total income and profit after tax) and four input variables (i.e. cost of labour, net fixed assets, current assets and other operating expenses) were selected for the purpose of the study. Findings DEA analysis has shown that 14 out of 37 hospitals are found to be efficient under the Cooper and Rhodes model of DEA and 20 out of 37 hospitals are efficient under the Banker, Charles and Cooper model of DEA. The empirical results pertaining to MPI indicate an overall productivity progress in the private Indian hospital industry during the study period, which is largely due to technological advancement in the industry. Tobit regression demonstrates that chain affiliated, specialized and multi-city located hospitals exhibit a higher technical efficiency. Research limitations/implications This study has a limitation with reference to the unavailability of data on the input and output parameters of the model. The data related to the number of beds, number of doctors, number of nurses, etc., were not available for the period under consideration. Originality/value This study seems to be one of the few studies applying productivity and performance analysis using DEA, MPI and Tobit regression for the Indian private hospital industry.

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Ghondaghsaz, Nassim, Asadollah Kordnaeij, and Jalil Delkhah. "Operational efficiency of plastic producing firms in Iran: a DEA approach." Benchmarking: An International Journal 25, no. 7 (October 1, 2018): 2126–44. http://dx.doi.org/10.1108/bij-01-2017-0011.

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Purpose Firms are working in a complex environment in which the updated information increase the pace of precise decision making and reduce the risk of wrong decisions. Therefore, discovering firms’ performance is a major issue. The purpose of this paper is to evaluate the efficiency of Iranian plastic producing companies by using data envelopment analysis (DEA). It also discovers various drivers that significantly affect the efficiency of enterprises. Design/methodology/approach The authors studied a sample of 17 manufacturing firms to examine the relative efficiency of companies. They, then, evaluated the effects of efficiency drivers and used two methods for these purposes: DEA and bootstrapped Tobit regression model. Findings The study has shown that two manufacturing firms out of selected 17 are efficient under the Charnes, Cooper, and Rhodes model. Also, nine out of 17 plastic producing companies are productive under the Banker, Charnes, and Cooper model. The results of Tobit regression shows that only two efficiency drivers out of four have a significant positive influence on the efficiency of plastic producing firms. Research limitations/implications Considering one industry and country limits the generalizability of the results provided. Besides, data availability has limited the analysis in some parts, particularly in bootstrapped Tobit regression. Practical implications The authors listed this section into benchmarking and strategical management; more importantly, the suggestions for improving the chemical industry and its future evolution are presented. Originality/value The paper is classified into two issues: the efficiency of plastic producing firms in Iran and evaluating the reason for inefficiency, apart from internal managerial procedures.

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H. Karim, Raz Muhammad, and Samira Muhamad Salh. "Using Tobit Model for Studying Factors Affecting Blood Pressure in Patients with Renal Failure." UHD Journal of Science and Technology 4, no. 2 (July 1, 2020): 1. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp1-9.

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In this study, the Tobit Model as a statistical regression model was used to study factors affecting blood pressure (BP) in patients with renal failure. The data have been collected from (300) patients in Shar Hospital in Sulaimani city. Those records contain BP rates per person in patients with renal failure as a response variable (Y) which is measured in units of millimeters of mercury (mmHg), and explanatory variables (Age [year], blood urea measured in milligram per deciliter [mg/dl], body mass index [BMI] expressed in units of kg/m2 [kilogram meter square], and Waist circumference measured by the Centimeter [cm]). The two levels of BP; high and low were taken from the patients. The mean arterial pressure (MAP) was used to find the average of both levels (high and low BP). The average BP rate of those patients equal to or >93.33 mmHg only remained in the dataset. The 93.33 mmHg is a normal range of MAP equal to 12/8 mmHg normal range of BP. The others have been censored as zero value, i.e., left censored. Furthermore, the same data were truncated from below. Then, in the truncated samples, only those cases under risk of BP (greater than or equal to BP 93.33mmHg) are recorded. The others were omitted from the dataset. Then, the Tobit Model applied on censored and truncated data using a statistical program (R program) version 3.6.1. The data censored and truncated from the left side at a point equal to zero. The result shows that factors age and blood urea have significant effects on BP, while BMI and Waist circumference factors have not to affect the dependent variable(y). Furthermore, a multiple regression model was found through ordinary least Square (OLS) analysis from the same data using the Stratigraphy program version 11. The result of (OLS) shows that multiple regression analysis is not a suitable model when we have censored and truncated data, whereas the Tobit model is a proficient technique to indicate the relationship between an explanatory variable, and truncated, or censored dependent variable.

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He, Hua, Wan Tang, Tanika Kelly, Shengxu Li, and Jiang He. "Statistical tests for latent class in censored data due to detection limit." Statistical Methods in Medical Research 29, no. 8 (November 18, 2019): 2179–97. http://dx.doi.org/10.1177/0962280219885985.

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Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.

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Dokas, Ioannis, Dimitris Giokas, and Anastasios Tsamis. "Liquidity Efficiency in the Greek Listed Firms." International Journal of Corporate Finance and Accounting 1, no. 1 (January 2014): 40–59. http://dx.doi.org/10.4018/ijcfa.2014010103.

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The scope of this paper is to investigate the liquidity efficiency of the Food and Beverage listed firms in the Athens Exchange, for the period 2006-2009, following a two stage analysis. In the first stage, Data Envelopment Analysis is employed to estimate liquidity efficiency for each firm of the sample. The results are consistent with the equivalent findings of the authors' analysis with financial ratios for the same period. The DEA analysis is validated by ordinary least squares (OLS) and the results prove that the regression equation can be used to evaluate a new liquidity efficiency for the firms of the specific sector. In the second stage of the analysis a Tobit regression model is applied in order to identify determinants of firms' liquidity efficiency. According to the authors' findings, a number of firms operating factors, like financial leverage, operating cost, firm size and Return on Equity to Return on Total Assets, have a significant impact on liquidity efficiency. In addition, the results of the Tobit regression model confirm the assumptions which are related to the links and their signs between liquidity efficiency and the aforementioned variables.

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Suslinawati, Suslinawati, Masyhuri Masyhuri, and Mas Soedjono. "ANALISIS PERMINTAAN TELUR BERDASARKAN JENIS DAN STRATA PENDAPATAN DI KALIMANTAN SELATAN." Agro Ekonomi 8, no. 1 (November 29, 2016): 60. http://dx.doi.org/10.22146/agroekonomi.16828.

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This research wanted to know the consumer behavior in demand of egg as source of animal protein in South Kalimantan. In particular, this research aimed to identify and to know the price elasticity, cross elasticity and income elasticity at low, middle and high income groups, which were divided into the rural and urban areas and also were divided into egg of layer, "buras" and duck.The data that was used in this research was SUSENAS data 1996, with 1698 surveyed households. The analysis used TOBIT model or the censored regression model. TOBIT model is a regression model that was used to estimate the qualitative difference between limit observation (zero) and non-limit observation (continuous).The result also showed that generally, the characteristic of demand of egg based on SUSENAS data 1996 in South Kalimantan was in-elastic except on demand of egg at low income stratum in rural area and on demand of egg of "buras", where the both of them were elastic. In accordance with SUSENAS data 1996, for South Kalimantan society, egg was normal good since all estimations showed that income elasticity was higher than zero and smaller than one

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Grmanová, Eva, and Ryszard Pukala. "Efficiency of insurance companies in the Czech Republic and Poland." Oeconomia Copernicana 9, no. 1 (March 31, 2018): 71–85. http://dx.doi.org/10.24136/oc.2018.004.

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Research background: The insurance market is an important part of the financial system. The international concept of insurance operations brings certainty and helps to balance the economic results. Purpose of the article: The aim of the paper is to compare the efficiency of life insurance of commercial insurance companies in the Czech Republic and Poland using the Data Envelopment Analysis model (DEA). Methods: The methods used in the paper are descriptive statistics, DEA model and Tobit regression model. DEA models are nonparametric quantitative models for efficiency assessment. They use linear programming to transform multiple inputs into multiple outputs. The efficiency of the subjects analyzed is assessed based on the efficiency scores. Findings & Value added: The subject of our analysis was 17 commercial insurance companies in the Czech Republic and 26 commercial insurance companies in Poland. Ten insurance companies were efficient on the common Czech-Polish insurance market. The share of efficient insurance companies was approximately equal. The arithmetic mean of the efficiency scores was higher in Poland than in the Czech Republic. The variability of the efficiency scores of Czech insurance companies was greater than the variability of the efficiency scores of Polish insurance companies. The correlations with efficiency score as the dependent variable and with market share as the independent variable of Tobit regression were very low.

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Ibrahim, Alisa, Nigel Driffield, and Keith Glaister. "THE DETERMINANTS OF OUTWARD FOREIGN DIRECT INVESTMENT FROM ASEAN." Humanities & Social Sciences Reviews 7, no. 2 (August 19, 2019): 434–48. http://dx.doi.org/10.18510/hssr.2019.7251.

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Purpose of Study: This study investigates the determinants of ASEAN outward foreign direct investment (OFDI) and the extent to which the four general motives of OFDI (market seeking, efficiency-seeking, resource-seeking, strategic-assets-seeking) can explain the phenomenon in the four chosen ASEAN countries (Malaysia, Singapore, Thailand and Indonesia). Methodology: We used panel data from 2001 – 2016 and the Tobit regression model to ascertain the results. We found that each country possibly has slightly different motives between each other although market seeking is seen as the general motive. As most of the previous studies focused on other developing countries such as BRICS, this study contributes to the small but growing literature of ASEAN economies. Furthermore, the usage of the Tobit regression Model helps us in explaining the variables with zero value, hence yielding a more informative result. Results: We found that, in general, some determinants were consistent with findings in the literature, while others need further investigation. Lastly, based on the findings, we can conclude that the mainstream theory of outward FDI applies to ASEAN.

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Ekstrand, C., and T. E. Carpenter. "Using a tobit regression model to analyse risk factors for foot-pad dermatitis in commercially grown broilers." Preventive Veterinary Medicine 37, no. 1-4 (December 1998): 219–28. http://dx.doi.org/10.1016/s0167-5877(98)00090-7.

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Journal articles on the topic 'Tobit regression model'

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