Bayesian model selection and parameter estimation for possibly asymmetric and non-stationary time series using a reversible jump Markov chain Monte Carlo approach

A Markov chain Monte Carlo (MCMC) approach, called a reversible jump MCMC, is employed in model selection and parameter estimation for possibly non-stationary and non-linear time series data. The non-linear structure is modelled by the asymmetric momentum threshold autoregressive process (MTAR) of Enders & Granger (1998) or by the asymmetric self-exciting threshold autoregressive process (SETAR) of Tong (1990). The non-stationary and non-linear feature is represented by the MTAR (or SETAR) model in which one ( 𝜌 1 ) of the AR coefficients is greater than one, and the other ( 𝜌 2 ) is smaller than one. The other non-stationary and linear, stationary and nonlinear, and stationary and linear features, represented respectively by ( 𝜌 1 = 𝜌 2 = 1 ), ( 𝜌 1 p 𝜌 2 < 1 ) and ( 𝜌 1 = 𝜌 2 < 1 ), are also considered as possible models. The reversible jump MCMC provides estimates of posterior probabilities for these four different models as well as estimates of the AR coefficients 𝜌 1 and 𝜌 2 . The proposed method is illustrated by analysing six series of US interest rates in terms of model selection, parameter estimation, and forecasting.     

Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method

Summary.  The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather-stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based on a spherical wavelet approach that has recently been developed for a multiscale representation and analysis of scattered data. Spatially adaptive estimators are obtained by coupling the spherical wavelets with different thresholding (selective reconstruction) techniques. These estimators are compared for their spatial adaptability and extrapolation performance by using the surface air temperature data.     

Reconsidering ethnic media research: An argument for a diasporic identity framework

Since Robert Park’s foundational work on the ethnic press, the assimilation-pluralism paradigm has dominated research. This article proposes a new framework that combines ethnic media and diasporic media literatures to advance future scholarship. Ethnic media research can provide direction in better conceptualizing media of ethnic groups/diasporas and can reinforce the importance of racial context, and diasporic media research can address the limitations of the assimilation-pluralism framework and its inability to incorporate globalization, transnationalism, and hybridity. This article submits that ethnic media be conceptualized as specific to media produced by a diasporic or indigenous ethnic population within the host society and that future textual research on ethnic media engage a diasporic identity framework. The framework would (a) call attention to transnationalism; (b) call attention to local/transnational, ethnic/race, and inter-generational tensions; (c) focus on race as a salient and important local context for diasporic meaning; and (d) consider the site of production.     

Dealing with the Initial Observation in the LM Unit Root Test

This article proposes a linearly weighted unit root test with a new weighting scheme which reflects the trade-off in power between the ADF and LM tests regarding the initial value of a time series. Simulation results indicate that the proposed test has better power performance and works better than other available tests in the literature for a range of initial conditions.     

Building a health communication brand for University of Texas System tobacco control

Objective: Despite declining cigarette smoking rates in the US, there is a continued need for tobacco prevention education campaigns to reach young adults. Recognizing the need for improved tobacco control messaging, the University of Texas (UT) System engaged The University of Texas at Austin Center for Health Communication to develop a brand and message that would strengthen tobacco control efforts at its 14 institutions. Methods: This article describes the iterative process involved in creating a brand for tobacco control, including an environmental scan, identifying potential message themes, and creating and refining logos. Results: This article highlights the process of developing a system-wide tobacco control brand. Specifically, the process included coordinating an interdisciplinary team with content and design experts, and presenting ideas to stakeholders for serial feedback and refinement, among others. Conclusions: Ultimately, this project offers a model for other systems of higher education interested in pursuing similar initiatives.     

保护和开发青海农村文化资源探析——以湟中县鲁沙尔镇的民间手工制造业为例

青海著名旅游胜地塔尔寺所在地湟中县鲁沙尔地区拥有源远流长、多民族融合、色彩斑斓的民间手工制造业文化资源。通过对湟中县鲁沙尔镇的民俗文化资源考察发现,由于多种原因,一些民间手工制造业文化资源尚未得到保护和开发。因此,利用农村文化资源发展文化产业,一定要处理好农村文化资源的保护和发展特色文化产业的关系,才能实现民俗文化与文化产业的双赢。     

浅析高校资产的科学化管理

随着高等教育的快速发展以及学校改革与发展的深入,高校资产管理中的问题也更突出。为了使资产保值、增殖及维护其权益不受侵害,为了保障高校资产能发挥更大的效益,资产的管理制度、管理方式至关重要。针对高校资产管理现状,管理机制的创新和以网络为平台的信息化管理手段是科学化管理高校资产的必由之路。     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.     

Bayesian principal component analysis with mixture priors

A central issue in principal component analysis (PCA) is that of choosing the appropriate number of principal components to be retained. Bishop (1999a) suggested a Bayesian approach for PCA for determining the effective dimensionality automatically on the basis of the probabilistic latent variable model. This paper extends this approach by using mixture priors, in that the choice dimensionality and estimation of principal components are done simultaneously via MCMC algorithm. Also, the proposed method provides a probabilistic measure of uncertainty on PCA, yielding posterior probabilities of all possible cases of principal components.