A measure of slope-rotatability for second order response surface experimental designs

In the design of experiments for estimating the slope of a response surface, slope-rotatability is a desirable property. In this paper, a measure is introduced that enables us to assess the degree of slope-rotatability for a given response surface design. The measure takes the value zero if and only if the design is slope-rotatable, and becomes larger as the design deviates from a slope-rotatable design. Examples of applying this measure to some response surface designs are also given.     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow

In a smoothing spline model with unknown change-points, the choice of the smoothing parameter strongly influences the estimation of the change-point locations and the function at the change-points. In a tumor biology example, where change-points in blood flow in response to treatment were of interest, choosing the smoothing parameter based on minimizing generalized cross-validation (GCV) gave unsatisfactory estimates of the change-points. We propose a new method, aGCV, that re-weights the residual sum of squares and generalized degrees of freedom terms from GCV. The weight is chosen to maximize the decrease in the generalized degrees of freedom as a function of the weight value, while simultaneously minimizing aGCV as a function of the smoothing parameter and the change-points. Compared with GCV, simulation studies suggest that the aGCV method yields improved estimates of the change-point and the value of the function at the change-point.     

What makes hybrid insourcing successful: A new public–private partnership model for social welfare services

Outsourcing has been a key policy tool for delivering a range of social services, and regarded as more effective than insourcing or direct government provision. At the same time, it has also caused many delivery issues such as principal‐agent problems, a lack of policy coordination, and poor‐quality welfare services. While the pendulum continues to swing between insourcing and outsourcing, we aim to propose a new public–private partnership model called the “hybrid insourcing model” and examine which factors influence the performance of the model. In South Korea, around 2010, the local government in Namyangju City was the first to implement the “Hope Care Center model,” a kind of hybrid insourcing model, which has been praised for its innovation and widely emulated by central and local governments. Our analysis utilizes data collected between December 2017 and January 2018 from public sector employees and civilian staff in Namyangju and a comparable city, A. From this, we draw a number of implications, both for theory and for policy. We argue that, for public–private partnerships, active cooperation and equality are the biggest factors in contributing to positive performance. These work alongside leaders with a clear vision and with employees' positive attitude.     

Predictive factors of acculturation attitudes and social support among Asian immigrants in the USA

This study examines acculturation attitude and its relation to social support among Asian immigrants. The data were collected from 242 Korean, Indian and Filipino immigrants in the USA using the Acculturation Attitude Scale and the Social Support Index. Findings showed that Koreans had a less positive attitude toward acculturation than did Indian and Filipino participants. Acculturation attitude was positively correlated with educational level and English fluency. This study also found a negative correlation between acculturation attitude and social support. Social support from friends and English fluency were identified as significant predictor variables in determining the level of acculturation attitude. These findings not only contribute to social work education and practice, but also increase cultural sensitivity and awareness in working with these populations.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

Model selection for factor analysis: Some new criteria and performance comparisons

This paper derives Akaike information criterion (AIC), corrected AIC, the Bayesian information criterion (BIC) and Hannan and Quinn’s information criterion for approximate factor models assuming a large number of cross-sectional observations and studies the consistency properties of these information criteria. It also reports extensive simulation results comparing the performance of the extant and new procedures for the selection of the number of factors. The simulation results show the di?culty of determining which criterion performs best. In practice, it is advisable to consider several criteria at the same time, especially Hannan and Quinn’s information criterion, Bai and Ng’s IC_p2 and BIC₃, and Onatski’s and Ahn and Horenstein’s eigenvalue-based criteria. The model-selection criteria considered in this paper are also applied to Stock and Watson’s two macroeconomic data sets. The results differ considerably depending on the model-selection criterion in use, but evidence suggesting five factors for the first data and five to seven factors for the second data is obtainable.     

Stakeholder perceptions of contextual factors related to PBIS implementation in high need schools

Positive behavior intervention and supports (PBIS) is a thoroughly researched three-tiered framework for preventing and treating challenging behavior in schools. While more than 20 years of research contribute to the literature base, less is known about PBIS implementation in high-needs school settings, particularly those that serve students predominantly from low-income families and majority minority student populations across urban and rural districts. This study investigated the critical contextual factors of PBIS implementation from the stakeholder perspective. Specifically, we surveyed 161 participants including teachers, school staff members, and administrators who were PBIS team members and had received technical assistance in PBIS implementation. The completed surveys were analyzed with participants and their schools coded for district type, Title I funding, and position in order to determine how the perceptions of issues such as buy-in, administrator support, PBIS in secondary school settings, and poverty affect PBIS implementation and team needs. The factor analysis and resulting structure along with statistically significant results for each factor are presented along with implications.