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We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

In this chapter, an air cargo shipment planning problem is considered by including individual risk factors of any sub-contracted agents. Due to competitive market conditions, air cargo forwarders are advised to remain flexible in operations. A mixed integer linear programming formulation including the potential for divisible activities is developed to model the shipment planning problem. To solve this complicated problem, we employ an ant colony optimization (ACO) methodology. Numerical examples are generated using data from both the extant literature and from a global air cargo company, allowing investigation of the viability of the novel methodology. We find that the algorithm/methodology provides effective solutions for small problem sizes.

This chapter is devoted to the study of the standard control of the material costs of production as a factor of technical growth of an enterprise of manufacturing industries: ways of integration in a single information space of information that allows you to manage the cost and quality of products at all stages of the value creation; theoretical and methodological approaches to the implementation of control functions of material costs (technological waste and production losses) according to preestablished consumption rates in the value creation of an industrial enterprise, especially in a number of machine-building factories of Nizhny Novgorod industrial cluster; theoretical methods of synthesis and comparison data, structured description of characteristics of the object of study, system analysis, as well as empirical methods of observations and descriptions of the subject area. The necessity of further improvement of effectiveness and methodology of a control function of a standard cost accounting system in the aspect of maintaining the technical growth of an enterprise is substantiated. Current condition is analyzed, and ways of modernization of organizational, managerial, methodological, metrological, and competence types are proposed. Also, sets of conditions for implementation of standard control and procedures incorporated into instrumental information space are substantiated. It is proved that the application of standard accounting of material costs for production within the framework of a system of standard accounting effectively contributes to noticeable technical growth and meets modern criteria and parameters of an effective management. It is substantiated that conditions for the implementation of a standard control of material costs for the production of an industrial enterprise that contributes to technical growth are availability and comprehensiveness of organizational, managerial, methodological, and metrological types of security control of material resources in the production; compliance with technical standards for processing, warehousing, storage, transportation of material resources; and matching competencies of management personnel with their functions. It is expected to introduce a standard control of material costs within the framework of a standard accounting system in practices of machine-building enterprises, as a significant factor of technical growth. Scientific understanding of an essence of the standard control of material costs within the framework of a standard cost accounting system, justified and outlined by the author, ensures incorporation into practice of effective production management, contributes a noticeable technical growth of industrial enterprises of the processing industries; provides a significant improvement in a quality of accounting results in general.

In a Bayesian approach, we compare the forecasting performance of five classes of models: ARCH, GARCH, SV, SV-STAR, and MSSV using daily Tehran Stock Exchange (TSE) market data. To estimate the parameters of the models, Markov chain Monte Carlo (MCMC) methods is applied. The results show that the models in the fourth and the fifth class perform better than the models in the other classes.

We analyze stochastic volatility effects in the context of the bond market. The short rate model is of Vasicek type and the focus of our analysis is the effect of multiple scale variations in the volatility of this model. Using a combined singular-regular perturbation approach we can identify a parsimonious representation of multiscale stochastic volatility effects. The results are illustrated with numerical simulations. We also present a framework for model calibration and look at the connection to defaultable bonds.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.