PREDICTION OF FREIGHT TURNOVER OF RAILWAY TRANSPORT USING REGRESSION MODELS WITH DETERMINISTIC AND STOCHASTIC EXPLANATORY VARIABLES

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

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Formulating Intuitionistic Fuzzy Regression Models: A Mathematical Programming Approach

10.21203/rs.3.rs-681349/v1 ◽

2021 ◽

Author(s):

Sheng-Hsing Nien ◽

Liang-Hsuan Chen

Keyword(s):

Mathematical Programming ◽

Regression Models ◽

Fuzzy Regression ◽

Distance Measures ◽

Programming Approach ◽

Mathematical Programming Problem ◽

Explanatory Variables ◽

Intuitionistic Fuzzy ◽

Mathematical Programming Approach ◽

Ordinary Regression

Abstract This study develops a mathematical programming approach to establish intuitionistic fuzzy regression models (IFRMs) by considering the randomness and fuzziness of intuitionistic fuzzy observations. In contrast to existing approaches, the IFRMs are established in terms of five ordinary regression models representing the components of the estimated triangular intuitionistic fuzzy response variable. The optimal parameters of the five ordinary regression models are determined by solving the proposed mathematical programming problem, which is linearized to make the resolution process efficient. Based on the concepts of randomness and fuzziness in the formulation processes, the proposed approach can improve on existing approaches’ weaknesses with establishing IFRMs, such as the limitation of symmetrical triangular membership (or non-membership) functions, the determination of parameter signs in the model, and the wide spread of the estimated responses. In addition, some numerical explanatory variables included in the intuitionistic fuzzy observations are also allowed in the proposed approach, even though it was developed for intuitionistic fuzzy observations. In contrast to existing approaches, the proposed approach is general and flexible in applications. Comparisons show that the proposed approach outperforms existing approaches in terms of similarity and distance measures.

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PP014 Will A Proposed Policy In Japan, Health Gold License, Work?

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462317002069 ◽

2017 ◽

Vol 33 (S1) ◽

pp. 74-74

Author(s):

Yusuke Nakamura ◽

Yoshiyuki Kuno ◽

Daiki Kanazawa ◽

Kosuke Iwasaki ◽

Tomomi Takeshima ◽

...

Keyword(s):

Data Center ◽

Financial Incentives ◽

Regression Models ◽

Claims Data ◽

Health Management ◽

Medical Costs ◽

Medical Checkup ◽

Explanatory Variables ◽

Dummy Variable ◽

To Receive

INTRODUCTION:Mr. Shinjiro Koizumi and some younger members of Japan's National Diet suggested a new policy, “Health Gold License” which would introduce financial incentives to encourage population health management, with people receiving medical checkups receiving a reduction in coinsurance from the current 30 percent to 20 percent. In this research, to evaluate the policy, we adjusted confounding factors of those insured who receive medical checkups (Medical-Checkup Group) and those who do not (Non-Medical-Checkup Group) using claims data, and estimated the effect of medical checkups on medical costs.METHODS:We analyzed Japanese employee-based claims data provided by the Japan Medical Data Center Co. Ltd. for the 3 million insured from January 2005 to December 2015. Two regression models were developed. Under model A, explanatory variables were year, age, dummy variables for various hierarchical condition categories and for medical checkups. Under model B, explanatory variables were estimated medical costs per patient per month (PMPM) in 2012 and a dummy variable for medical checkups. We also simulated the financial impact if Japan introduced Health Gold License for all insured.RESULTS:The coefficients of medical checkups in model A and in model B were -JPY4,816 PMPM and -JPY8,735 PMPM, respectively. The gap of medical costs between the Medical-Checkup Group and Non-Medical-Checkup Group was JPY4,588 PMPM, without any adjustment. If all of those insured received medical checkups, the breakeven coinsurance would be 27.2 percent.CONCLUSIONS:The Medical-Checkup Group is less expensive than Non-Medical-Checkup Group by at least 30%, therefore, the break-even coinsurance for them would be 0 percent. However, because most of those insured have already gone to medical check-ups every year, if the coinsurance were reduced from 30 percent to 20 percent for all insured, the finance would be largely negative. The break-even as 27.2 percent, we believe, would not incentivize the Non-Medical-Checkup Group to receive medical checkups. Therefore, the coinsurance reduction proposed under Health Gold License is not fully justified financially.

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The graphical evaluation of explanatory variables in proportional hazard regression models

Biometrika ◽

10.1093/biomet/68.1.93 ◽

1981 ◽

Vol 68 (1) ◽

pp. 93-98 ◽

Cited By ~ 50

Author(s):

S. W. LAGAKOS

Keyword(s):

Regression Models ◽

Proportional Hazard ◽

Hazard Regression ◽

Explanatory Variables

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Common Factors and Local Factors in Variance Risks: Evidence from S&P500 and KOSPI200 Index Options

Journal of Derivatives and Quantitative Studies ◽

10.1108/jdqs-02-2012-b0001 ◽

2012 ◽

Vol 20 (2) ◽

pp. 133-164

Author(s):

Sun-Joong Yoon ◽

So Hyun Kang

Keyword(s):

Interest Rates ◽

Regression Models ◽

Common Factor ◽

Higher Moments ◽

Bond Markets ◽

Index Options ◽

Explanatory Variables ◽

Local Factor ◽

Local Factors ◽

The Common

This paper conducts a factor analysis using the implied variances of S&P 500 index options and KOSPI 200 index options. After estimating the factors that influence variance risks, we rotate the factors to decompose them into a common factor and local factors. The results show that 10~12 percent of variance risks in both markets is explained by the common factor and 65 percent of S&P 500 implied variances and 70 percent of KOSPI 200 implied variances are explained by each local factor, which is in contrast to the results for bond markets that the most variation of interest rates could be explained by a common factor. To figure out the source of common and local factors, additionally, we adopt the regression models that incorporate the risk-neutral (RN) variance, skewness, and kurtosis as explanatory variables. Approximately, the common factor is mainly determined by the RN variance of the S&P 500 index and RN higher moments of the KOSPI 200 index. In contrast, the S&P 500 local factor is influenced by the RN variance of the S&P 500 index and the KOSPI 200 local factor is explained by the RN higher moment of the KOSPI 200 index.

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A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching

Journal of Econometrics ◽

10.1016/j.jeconom.2015.06.014 ◽

2016 ◽

Vol 190 (2) ◽

pp. 252-266 ◽

Cited By ~ 13

Author(s):

Irina Murtazashvili ◽

Jeffrey M. Wooldridge

Keyword(s):

Regression Models ◽

Control Function ◽

Function Approach ◽

Switching Regression ◽

Explanatory Variables ◽

Endogenous Switching ◽

Control Function Approach ◽

Switching Regression Models

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Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables

10.20944/preprints201608.0144.v1 ◽

2016 ◽

Author(s):

Yanjie Zeng ◽

Xiaofei Wang ◽

Lineng Liu ◽

Xinwei Li ◽

Caifeng Jiang

Keyword(s):

Regression Models ◽

Negative Binomial ◽

Model Parameters ◽

Horizontal Curve ◽

Frequency Model ◽

Crash Prediction ◽

Explanatory Variables ◽

Crash Prediction Model ◽

Report Data ◽

Effective Use

Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

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ECONOMETRIC MODELLING OF THE PASSENGER THROUGHPUT OF RUSSIAN CIVIL AVIATION

Russian Journal of Management ◽

10.12737/17903 ◽

2016 ◽

Vol 4 (1) ◽

pp. 54-64

Author(s):

Нина Горидько ◽

Nina Goridko ◽

Татьяна Соломина ◽

Tatyana Solomina

Keyword(s):

Regression Models ◽

Civil Aviation ◽

Short Term ◽

Average Income ◽

Factors Affecting ◽

Explanatory Variables ◽

Independent Variables ◽

Term Forecast ◽

Per Capita ◽

Two Independent Variables

The article is devoted to dynamics analysis and regression modelling of the civil aviation passenger throughput. There has been analyzed a relation between the passenger throughput and two independent variables: average income per capita and cost of economy class cabin flight per 1000 km. The authors drew substantial conclusions on the character of the relation between the dependent and explanatory variables, dynamics of air-passenger operations as well as the factors affecting the passenger throughput value. Based on constructed regression models a short-term forecast of explanatory variables and passenger throughput value has been made.

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Modelling Interaction Effects by Using Extended WOE Variables with Applications to Credit Scoring

Mathematics ◽

10.3390/math9161903 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1903

Author(s):

Carlos Giner-Baixauli ◽

Juan Tinguaro Rodríguez ◽

Alejandro Álvaro-Meca ◽

Daniel Vélez

Keyword(s):

Regression Models ◽

Computational Study ◽

Classification Tree ◽

Credit Scoring ◽

Logistic Models ◽

Interaction Effects ◽

Weight Of Evidence ◽

Logistic Regression Models ◽

Explanatory Variables ◽

Decision Making Processes

The term credit scoring refers to the application of formal statistical tools to support or automate loan-issuing decision-making processes. One of the most extended methodologies for credit scoring include fitting logistic regression models by using WOE explanatory variables, which are obtained through the discretization of the original inputs by means of classification trees. However, this Weight of Evidence (WOE)-based methodology encounters some difficulties in order to model interactions between explanatory variables. In this paper, an extension of the WOE-based methodology for credit scoring is proposed that allows constructing a new kind of WOE variable devised to capture interaction effects. Particularly, these new WOE variables are obtained through the simultaneous discretization of pairs of explanatory variables in a single classification tree. Moreover, the proposed extension of the WOE-based methodology can be complemented as usual by balance scorecards, which enable explaining why individual loans are granted or not granted from the fitted logistic models. Such explainability of loan decisions is essential for credit scoring and even more so by taking into account the recent law developments, e.g., the European Union’s GDPR. An extensive computational study shows the feasibility of the proposed approach that also enables the improvement of the predicitve capability of the standard WOE-based methodology.

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Count regression models for under-five deaths in rural Ethiopia

10.21203/rs.2.11962/v1 ◽

2019 ◽

Author(s):

Yenew Alemu mihret

Keyword(s):

Regression Models ◽

Wealth Index ◽

Health Policies ◽

Public Health Policies ◽

Explanatory Variables ◽

Under Five ◽

Rural Ethiopia ◽

Main Target ◽

Mother’S Age ◽

Working Region

Abstract Under-five mortality is defined as the likelihood for a child born alive to die between birth and fifth birth day. Mortality of under the age of five has been the main target of public health policies and is a common indicator of mortality levels, especially in developing countries. It is also viewed as an indicator of the level of development, health and socioeconomic status of the population. The objective of this study was to identify determinants of under-five mortality in Ethiopia using the 2011 EDHS data. To achieve the objective of this study descriptive statistics and count regression models were used for data analysis using socio-economic, demographic and environmental related variables as explanatory variables and the number of under-five deaths per mother as the response variables. According to Ethiopian Demography health Survey, 2011 report the level of under-five mortality in rural parts of Ethiopia is 114 deaths per 1000 live births. Factors influencing the number of under-five deaths have been identified. The study revealed that mother’s age at the first birth, breastfeeding status, wealth index, current mother working, region and mother’s level of education had statistically significant on the number of under-five deaths in rural parts of Ethiopia.

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