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Endogeneity or nonorthogonality in discrete choice models occurs when the systematic part of the utility is correlated with the error term. Under this misspecification, the model's estimators are inconsistent. When endogeneity occurs at the level of each observation, the principal technique used to treat for it is the control-function method, where a function that accounts for the endogenous part of the error term is constructed and is then included as an additional variable in the choice model. Alternatively, the latent-variable method can also address endogeneity. In this case, the omitted quality attribute is considered as a latent variable and modeled as a function of observed variables and/or measured through indicators. The link between the controlfunction and the latent-variable methods in the correction for endogeneity has not been established in previous work. This paper analyzes the similarities and differences between a set of variations of both methods, establishes the formal link between them in the correction for endogeneity, and illustrates their properties using a Monte Carlo experiment. The paper concludes with suggestions for future lines of research in this area.

This study aims to examine a second-order latent variable of family functioning built from two established protective factors for American Indian (AI) youth, i.e. family cohesion and parental monitoring. This study then examines if family functioning is related to alcohol use frequency or age of initiation for AI youth. Additionally, this study examines if family functioning served as a moderator for the risk factor of peer alcohol use.

Data came from the 2021 Our Youth, Our Future survey. Participants were 4,373 AI adolescents from Grades 6–12 across the contiguous USA. Structural equation modeling (SEM) was used to test the latent variable of family functioning. Structural paths and interaction terms between peer use and family functioning were added to the SEM to explore direct and moderating effects.

Family cohesion and parental monitoring were best represented by a second-order latent variable of family functioning, which was related to later initiation and lower alcohol use frequency.

The findings regarding the initiation of alcohol use may be applicable to prevention programs, with family functioning serving as a protective factor for the initiation of alcohol use. Programs working toward alcohol prevention may be best served by focusing on family-based programs.

The latent variable of family functioning is appropriate for use in AI samples. Family functioning, which is an inherent resilience factor in AI communities, was shown to be protective against harmful alcohol use behaviors.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

Technological and social planning often has unanticipated outcomes. The planned function of information technology is primarily information and storage. The latent function might be (1) an increase in Power for those able to make best use of this technology; (2) an acceleration of historic change in which multinational corporations may supplant nation‐states. The paper also speculates on unplanned changes in (1) the direction research takes; (2) library training; (3) accessibility of source materials; (4) the formation of library élites.

A phase‐change temperature‐based formulation including general latent heat effects is presented. These effects are taken into account by means of an explicit “phase‐change function” (or liquid fraction‐temperature relationship in a more specific context) defined analytically or based on experimental measurements. The behaviour of different functions is studied and compared. The finite element equations of this formulation are also described. Finally, a numerical example is analysed to illustrate the performance of the proposed methodology.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those scenarios where similarity among documents is computed considering semantic relationships among their terms. Kernel functions have received major attention because of the unprecedented popularity of SVMs in the field of TC. Most of the kernel functions exploit syntactic structures of the text, but quite a few also use a priori semantic information for knowledge extraction. The purpose of this paper is to investigate semantic kernel functions in the context of TC.

This work presents performance and accuracy analysis of seven semantic kernel functions (Semantic Smoothing Kernel, Latent Semantic Kernel, Semantic WordNet-based Kernel, Semantic Smoothing Kernel having Implicit Superconcept Expansions, Compactness-based Disambiguation Kernel Function, Omiotis-based S-VSM semantic kernel function and Top-k S-VSM semantic kernel) being implemented with SVM as kernel method. All seven semantic kernels are implemented in SVM-Light tool.

Performance and accuracy parameters of seven semantic kernel functions have been evaluated and compared. The experimental results show that Top-k S-VSM semantic kernel has the highest performance and accuracy among all the evaluated kernel functions which make it a preferred building block for kernel methods for TC and retrieval.

A combination of semantic kernel function with syntactic kernel function needs to be investigated as there is a scope of further improvement in terms of accuracy and performance in all the seven semantic kernel functions.

This research provides an insight into TC using a priori semantic knowledge. Three commonly used data sets are being exploited. It will be quite interesting to explore these kernel functions on live web data which may test their actual utility in real business scenarios.

Comparison of performance and accuracy parameters is the novel point of this research paper. To the best of the authors’ knowledge, this type of comparison has not been done previously.

The supervisory system of modern Chinese companies has basically adopted the supervisory board system of countries of continental law system. Certain disadvantages, however, exist in the system and the purpose of this paper is to identify those disadvantages and suggest changes.

This paper draws upon a consideration of the policy objectives and assesses the practical gaps in implementation through a consideration of case‐studies.

There are many functions and manifest functions of the supervisory board system of modern Chinese companies. Certain disadvantages, however, exist in this system: the postmortem nature of supervision causes the failure of timely supervision, and the externality of supervision leads to information asymmetry. Meanwhile, the functions of supervisory board are weakened by the fact that the law does not impose positive qualification requirements for supervisors and that the law has not laid down specific regulations for the effective deliverance of the company's financial and business information to the supervisors and the supervisory board. In order to guarantee the realization of the functions of the company's supervisory board, we should perfect in legislation the supervisory board system of our country, and at the same time strengthen the construction of company supervisory board system in our country and other relevant supervisory mechanism and supporting systems. Originality/value – This paper presents an analysis of “gaps” in policy implementation and might influence reform in this area.

Introduces the theory of artificial neural networks (ANN). Discusses its application to the valuation of residential apartments. Compares the performance of the back propagation neural network (BP) model in estimating sale prices of apartments against the traditional multiple regression analysis (MRA) model. Concludes that the neural network model is an easy‐to‐use, black‐box alternative to the MRA model.