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In the last two decades there has been considerable progress made in the development of alternative estimation techniques to ordinary least squares (OLS) regression. The search for alternative estimators has no doubt been motivated by the observance of erratic OLS estimator behavior in cases where there are too few observations, multicollinearity problems, or simply “information‐poor” data sets. Imprecise and unreliable OLS coefficient estimates have been the result.

The purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.

A mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.

The OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.

The crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.

In this manuscript, the authors aimed to demonstrate the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue.

In this manuscript, the authors demonstrated the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue. Furthermore, three different scenarios for crack growth are considered. In reality, edge-cracked plate, center-cracked plate and cracked plate in the presence of void and inclusion are studied. In fact, by designing suitable artificial neural network's (ANN) architectures all the three aforementioned conditions are trained and estimated through those architectures with very good agreement with input data. Also by conducting a series of sensitivity analysis, the most affecting factors in mixed-mode crack propagation in different situations are demonstrated. The obtained results are very interesting and useful for other researchers and also the authors hope the results would be cited by researchers.

The influential parameters on mixed-mode crack propagation were found in this paper.

The computer code using MATLAB was prepared to study the mixed-mode crack paths. Also using ANNs toolbox, the crack path estimation was investigated.

In this paper we use Monte Carlo sampling experiments to examine the properties of pretest estimators in the random parameters logit (RPL) model. The pretests are for the presence of random parameters. We study the Lagrange multiplier (LM), likelihood ratio (LR), and Wald tests, using conditional logit as the restricted model. The LM test is the fastest test to implement among these three test procedures since it only uses restricted, conditional logit, estimates. However, the LM-based pretest estimator has poor risk properties. The ratio of LM-based pretest estimator root mean squared error (RMSE) to the random parameters logit model estimator RMSE diverges from one with increases in the standard deviation of the parameter distribution. The LR and Wald tests exhibit properties of consistent tests, with the power approaching one as the specification error increases, so that the pretest estimator is consistent. We explore the power of these three tests for the random parameters by calculating the empirical percentile values, size, and rejection rates of the test statistics. We find the power of LR and Wald tests decreases with increases in the mean of the coefficient distribution. The LM test has the weakest power for presence of the random coefficient in the RPL model.

This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not.

To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis.

The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles.

To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment.

The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions.

The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.

This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

Development of new cultivars requires extensive genetic knowledge, trained personnel, and significant financial resources, so it is crucial for breeders to focus on the attributes most preferred by the key supply chain stakeholders such as consumers and producers. The purpose of this paper is to identify which attributes generate the highest total revenue or social surplus, information that breeders can take into account as they allocate resources to focus on attributes in their breeding programs.

This study used mail-in and online surveys to collect consumer and producer choice experiment data, and then employed mixed logit models to analyze and simulate individual producer and consumer willingness to pay (WTP) for the apple attributes.

Based on the simulation results, this study derived the supply and demand curves and the market equilibrium prices and quantities for each apple attribute. Based on the WTP analysis for both consumer and producer, this paper found the highest equilibrium price and welfare for apples come from crispness, followed by flavor.

The authors propose a framework to estimate the equilibrium prices and quantities of a product based on the results of choice experiments. The framework can be easily adapted to understand any countries’ producer and consumer preferences for certain products.