scholarly journals Representing Accessibility: Evidence from Vehicle Ownership Choices and Property Valuations in Singapore

2019 ◽  
Vol 2673 (2) ◽  
pp. 724-733 ◽  
Author(s):  
He He ◽  
Roberto Ponce-Lopez ◽  
Jingsi Shaw ◽  
Diem-Trinh Le ◽  
Joseph Ferreira ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Explanatory Power ◽  
Market Price ◽  
Real Estate Market ◽  
Vehicle Ownership ◽  
Explanatory Variables ◽  
Fit Statistics ◽  
Alternative Activities ◽  
Accessibility Measure ◽  
Better Than

This paper compares the relative performance of different measures of accessibility in relevant models. Specifically, the authors formulated three measures of accessibility: gravity-based accessibility, an aggregate measure of potential; trip-based accessibility, a disaggregate, utility-based measure of the value of travel alternatives; and activity-based accessibility, a theoretically richer disaggregate, utility-based measure of the value of alternative activities (including travel). These accessibility measures were used as explanatory variables in household vehicle ownership models and real estate market price models, comparing the explanatory power of each accessibility measure in each model as expressed by the confidence in the coefficient estimates and captured by the models’ goodness-of-fit statistics. It was found that trip-based accessibility best represents preferences for accessibility in both vehicle ownership decisions and property valuations. This supports the theoretical value of disaggregate, utility-based accessibility measures over aggregate, potential-based measures. The fact that trip-based measures perform better than activity-based accessibility measures underscores several empirical and technical limitations. Finally, the authors noted that accurately representing accessibility preferences requires congruence between the granularity of the accessibility measure and that of the explained behavior. This emphasizes the importance of understanding what accessibility measures actually capture and ensuring that they align with the analysis purpose.

An Assessment of White Spruce Tree Volume Equations in Canada

1990 ◽  
Vol 66 (6) ◽  
pp. 600-605 ◽  
Author(s):  
R. T. Morton ◽  
T. I. Grabowski ◽  
S. J. Titus ◽  
G. M. Bonnor
Keyword(s):  
Goodness Of Fit ◽  
White Spruce ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Single Model ◽  
Variable Model ◽  
Fit Statistics ◽  
Volume Equations ◽  
Better Than

In 1985, a survey of nine provinces and two territories was conducted to summarize operational tree volume estimation methods. Based on those results, six tree volume estimation functions were evaluated to answer the question: can a single model be used nation-wide for tree volume estimation? The six models were fitted to nation-wide data for 980 white spruce trees distributed nearly equally among the provinces and territories. Based on goodness of fit statistics and analysis of residuals, Schumacher's (1933) model and the Quebec combined variable model performed marginally better than the others. Further, the analyses did not reveal any significant differences between territories and provinces. It appears that any of these models could be applied to broad regions of Canada without suffering significant losses in accuracy.

scholarly journals Fit Adequacy of Dichotomous Logit Response Models of the Regressor Bernoulli and Binomial Probability Distributions

2016 ◽  
Vol 16 ◽  
pp. 38-48
Author(s):  
G. Uzodinma Ugwuanyim ◽  
Chukwudi Justin Ogbonna
Keyword(s):  
Goodness Of Fit ◽  
Probability Distributions ◽  
Probability Models ◽  
Response Models ◽  
Explanatory Variables ◽  
Fit Statistics ◽  
Binomial Distributions ◽  
Multiple Correlation Analysis ◽  
Work Done ◽  
Best Fit

Logit models belong to the class of probability models that determine discrete probabilities over a limited number of possible outcomes. They are often called ‘Quantal Variables’ or ‘Stimulus and Response Models’ in Biological Literature. The conventional R2 measure of goodness-of-fit is problematic in logit models. This has therefore led to the proposal of several alternative goodness-of-fit measures. But researchers in this area have identified the base rate problem in using these several alternative goodness-of-fit measures. This research is an extension of work done by people in this area. Specifically, this research is aimed at investigating the goodness-of-fit performances of eight statistics using the Bernoulli and Binomial distributions as explanatory variables under various scenarios. The study will draw conclusions on the “best” fit. The data for the study was generated through simulation and analysed using the multiple correlation analysis. The findings clearly show that for the Bernoulli Distribution, the goodness-of-fit statistics to use are: RO2, RC2, RM2 and λp; and for the Binomial Distribution, the goodness-of-fit statistics to use are: and RN2 and λp. RO2 stood out as the “best” goodness-of-fit statistics.

scholarly journals Modeling generalized statistical distributions of PM2.5 concentrations during the COVID-19 pandemic in Jakarta, Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 393-400
Author(s):  
Warsono Warsono ◽  
Yeftanus Antonio ◽  
Slamet B. Yuwono ◽  
Dian Kurniasari ◽  
Erdi Suroso ◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Evaluation ◽  
Goodness Of Fit ◽  
Scientific Evidence ◽  
Statistical Distributions ◽  
Effective Management ◽  
Fit Statistics ◽  
Generalized Distributions ◽  
Statistical Behavior ◽  
Better Than

Understanding the probabilistic or statistical behavior of air concentrations is necessary for the effective management of air pollution, such as PM2.5. Failure to consider the appropriateness of the model can lead to making inferences that are not supported by scientific evidence. The main focus of this article is to find the best statistical distribution in fitting PM2.5 concentrations in the periods of February–June 2018 and February–June 2019 (the periods without COVID-19) and in the period of February–June 2020 (the period with COVID-19) in Jakarta, Indonesia. This article considers making an assessment of the performance of both generalized distributions (e.g., generalized gamma, generalized extreme value, and generalized log-logistic [GLL]) and classical distributions (such as lognormal [LN], gamma, Weibull, log-logistic, and Gumbel) in modeling daily concentrations of PM2.5 in the period of February–June 2020, or the period during which the COVID-19 pandemic is present, in Jakarta. For comparison purposes, this study also analyzed PM2.5 concentrations in the periods of February–June 2018 and February–June 2019. The comparative evaluation of the models of each period of data uses graphical analyses and goodness-of-fit statistics. The results of applications indicate that the generalized distributions fit the data better than do the classical distributions. Particularly, compared with the classical distributions, including the LN model, the GLL distribution is the most appropriate model in fitting PM2.5 concentrations in the periods without and during the period with COVID-19 in Jakarta, Indonesia.

scholarly journals Engineering Inspection Associated Artificial Intelligence for Appraisal of the Property in Niteroi, Rio de Janeiro, Brazil

2021 ◽  
Vol 14 ◽  
pp. 69-77
Author(s):  
Vladimir Surgelas ◽  
Irina Arhipova ◽  
Vivita Pukite
Keyword(s):  
Fuzzy Logic ◽  
Rio De Janeiro ◽  
Civil Engineering ◽  
Market Price ◽  
Real Estate Market ◽  
Percentage Error ◽  
The Real Estate ◽  
Explanatory Variables ◽  
Market Values ◽  
The Real Estate Market

The construction sector is linked to the general development of a country. There is a lot of data scattered and not properly explored in relation to the buildings constructed. However, if these scattered data on the behavior of the real estate market are organized, combined with knowledge of civil engineering, this merger of information can mitigate some evaluation problems, especially those that are overvalued for unknown or dubious reasons. Thus, there is a need for models capable of working with limited data to analyze the causal relationships between explanatory variables and sales prices and, from there, predict property values. The purpose of this article is the innovative use of simple building inspection strategies to predict the market price for residential apartments. For this, 19 samples of residential apartments are used in the city of Niterói, Rio de Janeiro, Brazil, in February 2021. The methodology uses the results of the survey of civil engineering and converts them into heuristic terms predicting the price of the property. With this, the imprecision, uncertainty, and subjectivity of human expression combined with the knowledge of civil engineering result in a plausible solution and easy application in the market. Finally, the use of fuzzy logic in the evaluation of properties is an adequate unconventional method, in addition to avoiding repetition in regression coefficients in binary logic. To check the reliability of the method, the comparison between the market values of the samples and the values predicted by the fuzzy logic is used. The result according to the mean absolute percentage error (MAPE) can be interpreted as a good result (7%).

scholarly journals Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1930
Author(s):  
Kuang-Hua Hu ◽  
Shih-Kuei Lin ◽  
Yung-Kang Ching ◽  
Ming-Chin Hung
Keyword(s):  
Logistic Regression ◽  
Goodness Of Fit ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Estimation Model ◽  
Loss Given Default ◽  
Explanatory Variables ◽  
Gdp Growth Rate ◽  
Credit Default ◽  
Default Rate

Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

scholarly journals Using Ultrasound and Inflammation to Improve Prediction of Ischemic Stroke: A Secondary Analysis of the Multi-Ethnic Study of Atherosclerosis

2021 ◽  
pp. 37-43
Author(s):  
Hediyeh Baradaran ◽  
Alen Delic ◽  
Ka-Ho Wong ◽  
Nazanin Sheibani ◽  
Matthew Alexander ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Goodness Of Fit ◽  
Primary Outcome ◽  
Stroke Risk ◽  
Secondary Analysis ◽  
Predictor Variables ◽  
Baseline Model ◽  
Fit Statistics ◽  
Ischemic Stroke Risk

Introduction: Current ischemic stroke risk prediction is primarily based on clinical factors, rather than imaging or laboratory markers. We examined the relationship between baseline ultrasound and inflammation measurements and subsequent primary ischemic stroke risk. Methods: In this secondary analysis of the Multi-Ethnic Study of Atherosclerosis (MESA), the primary outcome is the incident ischemic stroke during follow-up. The predictor variables are 9 carotid ultrasound-derived measurements and 6 serum inflammation measurements from the baseline study visit. We fit Cox regression models to the outcome of ischemic stroke. The baseline model included patient age, hypertension, diabetes, total cholesterol, smoking, and systolic blood pressure. Goodness-of-fit statistics were assessed to compare the baseline model to a model with ultrasound and inflammation predictor variables that remained significant when added to the baseline model. Results: We included 5,918 participants. The primary outcome of ischemic stroke was seen in 105 patients with a mean follow-up time of 7.7 years. In the Cox models, we found that carotid distensibility (CD), carotid stenosis (CS), and serum interleukin-6 (IL-6) were associated with incident stroke. Adding tertiles of CD, IL-6, and categories of CS to a baseline model that included traditional clinical vascular risk factors resulted in a better model fit than traditional risk factors alone as indicated by goodness-of-fit statistics. Conclusions: In a multiethnic cohort of patients without cerebrovascular disease at baseline, we found that CD, CS, and IL-6 helped predict the occurrence of primary ischemic stroke. Future research could evaluate if these basic ultrasound and serum measurements have implications for primary prevention efforts or clinical trial inclusion criteria.

scholarly journals Choosing Factors for the Vietnamese Stock Market

2021 ◽  
Vol 14 (3) ◽  
pp. 96
Author(s):  
Nina Ryan ◽  
Xinfeng Ruan ◽  
Jin E. Zhang ◽  
Jing A. Zhang
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Five Factor Model ◽  
Explanatory Power ◽  
Factor Models ◽  
Marginal Contribution ◽  
Return On Equity ◽  
Factor Modeling ◽  
Three Factor ◽  
Better Than

In this paper, we test the applicability of different Fama–French (FF) factor models in Vietnam, we investigate the value factor redundancy and examine the choice of the profitability factor. Our empirical evidence shows that the FF five-factor model has more explanatory power than the FF three-factor model. The value factor remains important after the inclusion of profitability and investment factors. Operating profitability performs better than cash and return-on-equity (ROE) profitability as a proxy for the profitability factor in FF factor modeling. The value factor and operating profitability have the biggest marginal contribution to a maximum squared Sharpe ratio for the five-factor model factors, highlighting the value factor (HML) non-redundancy in describing stock returns in Vietnam.

Extreme price moves: an INGARCH approach to model coexceedances in commodity markets

2020 ◽  
Author(s):  
Bernardina Algieri ◽  
Arturo Leccadito
Keyword(s):  
Goodness Of Fit ◽  
Emerging Market ◽  
Explanatory Power ◽  
Commodity Markets ◽  
Credit Spread ◽  
Market Demand ◽  
Factors Affecting ◽  
Monday Effect ◽  
Tail Events ◽  
Weather Anomalies

Abstract This study presents a set of integer-valued generalised autoregressive conditional heteroskedastic models to identify possible transmission channels of joint extreme price moves (coexceedances) across a group of agricultural commodities. These models are very useful to identify factors affecting joint tail events and they are superior in terms of goodness of fit to models without autoregressive components. Emerging market demand, crude oil, exchange rate, stock market conditions and credit spread explain extreme joint returns. Psychological factors and the Monday effect play a role in affecting extreme events, while weather anomalies (El Niño and La Niña episodes) do not have explanatory power.

scholarly journals An Universal, Simple, Circular Statistics-Based Estimator of α for Symmetric Stable Family

2019 ◽  
Vol 12 (4) ◽  
pp. 171
Author(s):  
Ashis SenGupta ◽  
Moumita Roy
Keyword(s):  
Entire Range ◽  
Goodness Of Fit ◽  
Stable Distribution ◽  
Real Life ◽  
Circular Statistics ◽  
Directional Statistics ◽  
Symmetric Stable Distribution ◽  
Stable Family ◽  
Family Of Distributions ◽  
Better Than

The aim of this article is to obtain a simple and efficient estimator of the index parameter of symmetric stable distribution that holds universally, i.e., over the entire range of the parameter. We appeal to directional statistics on the classical result on wrapping of a distribution in obtaining the wrapped stable family of distributions. The performance of the estimator obtained is better than the existing estimators in the literature in terms of both consistency and efficiency. The estimator is applied to model some real life financial datasets. A mixture of normal and Cauchy distributions is compared with the stable family of distributions when the estimate of the parameter α lies between 1 and 2. A similar approach can be adopted when α (or its estimate) belongs to (0.5,1). In this case, one may compare with a mixture of Laplace and Cauchy distributions. A new measure of goodness of fit is proposed for the above family of distributions.

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