University of Houston

About: University of Houston is a education organization based out in Houston, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23074 authors who have published 53903 publications receiving 1641968 citations.

Structural equation modeling in marketing: Some practical reminders

Abstract: The authors review issues related to the application of structural equation modeling (SEM) in marketing. The discussion begins by considering issues related to the process of applying SEM in empirical research, including model specification, identification, estimation, evaluation, and respecification, and reporting of results. In addition to these process issues, a number of other issues, such as formulation of multiple theoretical models, model error versus sampling error, and relating study objectives to the capabilities of SEM, are considered, and suggestions offered regarding ways that SEM applications might be improved.

Social Networks, Gender, and Immigrant Incorporation: Resources and Constraints

Abstract: Most research on social networks and immigrant incorporation focuses on the short-term and positive functions of networks, neglecting changes in networks over time. Author present a dynamic and variable portrayal of networks to demonstrate how they gradually assume different forms and functions for women and for men that differentially affect settlement outcomes, particularly opportunities to become legal. The gendered social relations of neighborhood, work, and voluntary associations interact to produce this outcome. The conclusions suggest that social networks can both strengthen and weaken over time, can change differentially for different segments of the immigrant community, and therefore can have disparate effects on incorporation.

Quantile smoothing splines

Abstract: SUMMARY Although nonparametric regression has traditionally focused on the estimation of conditional mean functions, nonparametric estimation of conditional quantile functions is often of substantial practical interest. We explore a class of quantile smoothing splines, defined as solutions to

Tuning the Magnetic Properties of Nanoparticles

Abstract: The tremendous interest in magnetic nanoparticles (MNPs) is reflected in published research that ranges from novel methods of synthesis of unique nanoparticle shapes and composite structures to a large number of MNP characterization techniques, and finally to their use in many biomedical and nanotechnology-based applications. The knowledge gained from this vast body of research can be made more useful if we organize the associated results to correlate key magnetic properties with the parameters that influence them. Tuning these properties of MNPs will allow us to tailor nanoparticles for specific applications, thus increasing their effectiveness. The complex magnetic behavior exhibited by MNPs is governed by many factors; these factors can either improve or adversely affect the desired magnetic properties. In this report, we have outlined a matrix of parameters that can be varied to tune the magnetic properties of nanoparticles. For practical utility, this review focuses on the effect of size, shape, composition, and shell-core structure on saturation magnetization, coercivity, blocking temperature, and relaxation time.