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1 – 10 of over 24000Abhijat Arun Abhyankar and Harish Kumar Singla
The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression…
Abstract
Purpose
The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”
Design/methodology/approach
Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).
Findings
While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).
Research limitations/implications
The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.
Practical implications
The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.
Originality/value
To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.
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Yang Li and Tianxiang Lan
This paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual…
Abstract
Purpose
This paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual parameters.
Design/methodology/approach
This analysis is based on the numerical simulation data obtained, using the hybrid finite element–discrete element (FE–DE) method. The forecasting model was compared with the numerical results and the accuracy of the model was evaluated by the root mean square (RMS) and the RMS error, the mean absolute error and the mean absolute percentage error.
Findings
The multivariate nonlinear regression model can accurately predict the nonlinear relationships between injection rate, leakoff coefficient, elastic modulus, permeability, Poisson’s ratio, pore pressure and final fracture area. The regression equations obtained from the Newton iteration of the least squares method are strong in terms of the fit to the six sensitive parameters, and the model follow essentially the same trend with the numerical simulation data, with no systematic divergence detected. Least absolutely deviation has a significantly weaker performance than the least squares method. The percentage contribution of sensitive parameters to the final fracture area is available from the simulation results and forecast model. Injection rate, leakoff coefficient, permeability, elastic modulus, pore pressure and Poisson’s ratio contribute 43.4%, −19.4%, 24.8%, −19.2%, −21.3% and 10.1% to the final fracture area, respectively, as they increased gradually. In summary, (1) the fluid injection rate has the greatest influence on the final fracture area. (2)The multivariate nonlinear regression equation was optimally obtained after 59 iterations of the least squares-based Newton method and 27 derivative evaluations, with a decidability coefficient R2 = 0.711 representing the model reliability and the regression equations fit the four parameters of leakoff coefficient, permeability, elastic modulus and pore pressure very satisfactorily. The models follow essentially the identical trend with the numerical simulation data and there is no systematic divergence. The least absolute deviation has a significantly weaker fit than the least squares method. (3)The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.
Originality/value
The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.
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Dante Amengual, Gabriele Fiorentini and Enrique Sentana
The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several…
Abstract
The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.
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Xichen Chen, Alice Yan Chang-Richards, Tak Wing Yiu, Florence Yean Yng Ling, Antony Pelosi and Nan Yang
With growing concern about sustainable development and increased awareness of environmental issues, digital technologies (DTs) are gaining prominence and becoming a promising…
Abstract
Purpose
With growing concern about sustainable development and increased awareness of environmental issues, digital technologies (DTs) are gaining prominence and becoming a promising trend to improve productivity, sustainability and project performance in the construction industry. Nonetheless, the uptake of DTs in the construction industry has been limited and plagued with roadblocks. This study aims to identify critical barriers for construction organisations to adopt DTs and to demonstrate relationships between organisational characteristics and the perceived DTs adoption barriers.
Design/methodology/approach
This study adopted an explanatory sequential design by combining the advantages of quantitative and qualitative data. Data collection methods include literature review, a pilot study, questionnaire survey, and semi-structured interviews. Questionnaire data were analysed by using SPSS and multivariate regression technique. The interview data were processed by using content analysis to validate and supplement findings from the questionnaire.
Findings
Based on the survey and interview results, eight critical barriers were identified: the three top critical barriers are (1) “status quo industry standards”, (2) “lack of client interest” and (3) “lack of financial need/drive for using DTs”. The eight critical barriers were further classified into technical, environmental, and social dimensions to determine the major constructs that hinder DTs adoption. A theoretical framework articulating critical barriers with underlying components and root causes was also proposed. Furthermore, by using multivariate regression analysis, a model was developed to link the organisational characteristics with barriers to DTs adoption.
Practical implications
By referring to the framework and the model developed, academics, industry practitioners, and decision makers can identify pivotal areas for improvement, make informed decisions and implement remedial measures to remove the barriers to digitalisation transformation.
Originality/value
This study contributes to the literature on construction innovations by investigating barriers to DTs adoption holistically as well as perceptions of the impact of organisational attributes on these barriers. It establishes the groundwork for future empirical research into the strategic consolidation of movement of DTs adoption and diffusion.
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Jeh-Nan Pan, Chung-I Li and Jun-Wei Hsu
The purpose of this paper is to provide a new approach for detecting the small sustained process shifts in multistage systems with correlated multiple quality characteristics.
Abstract
Purpose
The purpose of this paper is to provide a new approach for detecting the small sustained process shifts in multistage systems with correlated multiple quality characteristics.
Design/methodology/approach
The authors propose a new multivariate linear regression model for a multistage manufacturing system with multivariate quality characteristics in which both the auto-correlated process outputs and the correlations occurring between neighboring stages are considered. Then, the multistage multivariate residual control charts are constructed to monitor the overall process quality of multistage systems with multiple quality characteristics. Moreover, an overall run length concept is adopted to evaluate the performances of the authors’ proposed control charts.
Findings
In the numerical example with cascade data, the authors show that the detecting abilities of the proposed multistage residual MEWMA and MCUSUM control charts outperform those of Phase II MEWMA and MCUSUM control charts. It further demonstrates the usefulness of the authors’ proposed control charts in the Phase II monitoring.
Practical implications
The research results of this paper can be applied to any multistage manufacturing or service system with multivariate quality characteristics. This new approach provides quality practitioners a better decision making tool for detecting the small sustained process shifts in multistage systems.
Originality/value
Once the multistage multivariate residual control charts are constructed, one can employ them in monitoring and controlling the process quality of multistage systems with multiple characteristics. This approach can lead to the direction of continuous improvement for any product or service within a company.
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The purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption.
Abstract
Purpose
The purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption.
Design/methodology/approach
The EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference.
Findings
The authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t-distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen’s αs, and for testing asset pricing models.
Practical implications
The results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management.
Originality/value
The authors proposed new procedures that makes it easy to use a multivariate t-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.
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Robert J. Kaminski and David W.M. Sorensen
Uses data on 1,550 nonlethal assaults recorded by Baltimore County Police Department. Examines factors that are associated with the likelihood of officer injury after an assault…
Abstract
Uses data on 1,550 nonlethal assaults recorded by Baltimore County Police Department. Examines factors that are associated with the likelihood of officer injury after an assault. Notes that factors affecting the probability of assault do not necessarily correspond with the factors that affect the likelihood of injury. Analyzes a broader spectrum of contributory factors than those addressed by other research. Finds inter alia that greater officer proficiency in unarmed defensive tactics may reduce their assault‐related injuries, since most incidents do not involve arms; that in‐service training should be biased toward less experienced officers who are at greater risk; that officer height is a significant variable; that many officers suffer multiple attacks; that domestic disturbances do not rank higher than other dangers, but that this may reflect the possibility that officers anticipate potential violence and take better precautions before attending the scene.
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Shihunegn Alemayehu, Ali Nejat, Tewodros Ghebrab and Souparno Ghosh
Building information modeling (BIM) is a process of creating an intelligent virtual model integrating project data from design to construction and operation. BIM models enhance…
Abstract
Purpose
Building information modeling (BIM) is a process of creating an intelligent virtual model integrating project data from design to construction and operation. BIM models enhance the process of communicating the progress of construction to stakeholders and facilitate integrated project delivery, coordination and clash detection. However, barriers within the construction industry in Ethiopia has led to slow BIM adoption in the country. The aim of this paper is to identify perceived BIM barriers, provide a platform to quantify their importance and develop a regression model to link individual's personal/professional attributes to their perception of BIM barrier.
Design/methodology/approach
To address the objectives of this research, an online survey was developed to collect feedback from construction professionals in Ethiopia on 20 major adoption barriers extracted from a thorough review of literature. Relative importance index and strength of consensus metric were employed to identify the significance of barriers. This was then succeeded by performing exploratory factor analysis to determine the major constructs of BIM barriers which was then used to develop a multivariate regression model linking respondents' personal attributes to their perception of BIM barrier.
Findings
Results revealed the importance of project complexity and BIM maturity level in prioritizing barriers that are more relevant under various contexts. More specifically, results indicated the following study highlights: Project complexity led to higher perceived weights for lack of appropriate physical/cloud infrastructures, and a BIM standard. Higher levels of BIM maturity signified the importance of BIM internal issues such as liability, licensing and maintenance issues among other adoption barriers. Female participants tended not to consider intangibility of BIM benefits as a major barrier towards BIM adoption compared to male participants. Age of the participants turned out to be the least important factor in their prioritization of BIM perceived adoption barriers.
Originality/value
While many research studies have explored BIM adoption barriers in various countries around the world, none to the best of the authors' knowledge have attempted to develop a model to highlight the impact of individuals' personal/professional attributes on their perception of adoption barriers within their community which can help with prioritizing the barriers that are deemed to be more important given the characteristics of the community under study. Our result indicated the importance of BIM maturity level and project complexity in prioritizing barriers associated with BIM adoption within Ethiopia's construction industry.
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Sam K. Formby, Manoj K. Malhotra and Sanjay L. Ahire
Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is…
Abstract
Purpose
Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is an increasing body of research based on complexity theory (CT) suggesting that constructs such as these should be viewed as variables in a complex system with inter-dependencies, interactions, and potentially nonlinear relationships. Despite the significant body of conceptual research related to CT, there is a lack of methodological research into these potentially nonlinear effects. The purpose of this paper is to demonstrate the theoretical and practical importance of non-linear terms in a multivariate polynomial model as they become more significant predictors of firm success in collaborative environments and less significant in more rigidly controlled work environments.
Design/methodology/approach
Multivariate polynomial regression methods are used to examine the significance and effect sizes of interaction and quadratic terms in operations scenarios expected to have varying degrees of complex and complex adaptive behaviors.
Findings
The results find that in highly collaborative work environments, non-linear and interaction effects become more significant predictors of success than the linear terms in the model. In more rigid, less collaborative work environments, these effects are not present or significantly reduced in effect size.
Research limitations/implications
This study shows that analytical methods sensitive to detecting and measuring nonlinearities in relationships such as multivariate polynomial regression models enhance our theoretical understanding of the relationships between constructs when the theory predicts that complex and complex adaptive behaviors are present and important.
Originality/value
This study demonstrates that complex adaptive behaviors between management and the workforce exist in certain environments and provide greater understanding of factor relationships relating to firm success than more traditional linear analytical methods.
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