Social Bonds and the Migration Intentions of Elderly Urban Residents: The Mediating Effect of Residential Satisfaction

Under the incorporation of social bonds within the residential satisfaction model, this study has attempted to examine why the elderly living in neighborhoods consider moving. The main hypothesis proposed in this study is that the four social bonds (friendship, social cohesion and trust, informal social control, and neighborhood activities), combined with residential satisfaction, affect the mobility intentions of elderly urban residents. This hypothesis is tested by survey data collected in 1995 from 1123 Chicago residents age 65 and over. The results support the hypothesis that strong social bonds, combined with residential satisfaction, are working as important factors when the mobility intentions of elderly urban residents are deterred.     

Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model

Seoul, the capital city of Korea with over 10 million residents, has been experiencing serious air pollution problems. Previous studies on source apportionment of PM2.5 in Seoul are based on measurements of chemical compositions of PM2.5 from a single monitoring site. In this paper, we analyse PM2.5 concentration data collected from multiple sites in 24 districts of Seoul and estimate regional source profiles using Bayesian multivariate receptor model. The regional source profiles provide information for the identification of major PM2.5 sources as well as the regions relatively more seriously affected by each source than other regions. These regional characteristics relevant to PM2.5 can help establish effective, customised, region-specific PM2.5 control strategies for each region rather than general strategies that apply to every region of Seoul.     

Cointegration and Structural Change: An Application to the U.S. Demand for Money

I perform the cointegration tests for the trivariate model of real monetary aggregates, real personal income, and short‐term interest rate. I find the existence of a long‐run relationship among these three variables. To check the stability of a long‐run money demand relationship, I implement a rank constancy test and CUSUM test. The test results show that real M1 is relatively more stable than other aggregates, but structural change occurred during the 1970s and early 1980s. This is consistent with prior research on money demand.     

Some asymptotic properties in INAR(1) processes with Poisson marginals

The first-order integer-valued autoregressive (INAR(1)) process with Poisson marginal distributions is considered. It is shown that the sample autocovariance function of the model is asymptotically normally distributed. We derive asymptotic distribution of Yule-Walker type estimators of parameters. It turns out that our Yule-Walker type estimators are better than the conditional least squares estimators proposed by Klimko and Nelson (1978) and Al-Osh and Alzaid (1987). also, we study the relationship between the model andM/M/∞ queueing system.     

“Crowding in” or “crowding out”? An examination of the impact of the welfare state on generalized social trust

Generalized social trust is correlated with increased levels of civic engagement, lower crime rates, and greater economic growth. Many scholars believe that equality provides the conditions in which social trust can flourish. Thus, welfare programs might be one way to generate social trust. However, the relationship between social spending and trust is contested: Some argue it is negative, while others argue it is positive. This study examined the effects of total social welfare expenditures on social trust in 18 OECD countries, holding constant individual characteristics, country characteristics, and country and year effects. Fixed effects analyses indicate that every additional percent of gross domestic product spent on social expenditures 5 years prior is associated with a 4.7 percent increased likelihood that respondents of that country will endorse trusting other people. Further testing for reverse causality found no significant association between trust and later social expenditures, supporting the claim that expenditures drive trust instead of the reverse.     

Automatic polynomial wavelet regression

In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for wavelet thresholding regression. The idea is to model the regression function as a sum of wavelet basis functions and a low-order polynomial. The latter is expected to account for the boundary problem. Practical implementation of this method requires the choice of the order of the low-order polynomial, as well as the wavelet thresholding value. This paper proposes two automatic methods for making such choices. Finite sample performances of these two methods are evaluated via numerical experiments.     

Bayesian analysis of panel data using an MTAR model

Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.     

A Note on the Influence of Rail Defects on the Risk Associated with Shipping Hazardous Materials by Rail

Existing approaches to routing hazardous material shipments by rail recognize that track condition is an important influence, but have not included it in the risk assessment and routing models. This note explores the influence of track condition based on predictions of internal defects in the rail. The method developed predicts the expected frequency of accidents and subsequent consequences in terms of the expected number of fatalities accounting for one aspect of track condition-internal defects. It is intended to indicate the magnitude and impact of track condition. The formulation integrates models of consequences and the risk of a hazardous spill found in the literature with the frequency of accidents as a function of the number of defects. The number of defects may be based on observations or predicted as a function of the cumulative traffic. The models are used to calculate the expected number of fatalities per year for a particular route. Application of the methodology to a hypothetical route shows that the risk associated with the transportation of hazardous material shipments varies significantly with the expected number of defects in the track. Therefore, risk not only varies from route to route but over time for any section of track as the condition deteriorates.     

Robust precision matrix estimation via weighted median regression with regularization

A precision matrix is an important parameter of interests because its elements describe useful association information among multiple variables, which has a wide variety of applications. For example, it is used for inferring gene regulation networks in genomic studies and stock association networks in financial studies. However, in many cases, the precision matrix needs to be robustly estimated due to the presence of outliers. We propose estimating a sparse scaled precision matrix via weighted median regression with regularization. Our weighted median regression approach is consistent under various distributional assumptions including multivariate t‐ or contaminated Gaussian distributions. This fact is illustrated with simulation studies and a real data analysis with monthly stock return data. The Canadian Journal of Statistics 46: 265–278; 2018 © 2018 Statistical Society of Canada     

Diagnostics for repeated measurements in generalized linear mixed effects models

ABSTRACT

As there is an extensive body of research on diagnostics in regression models, various outlier detection methods have been developed. These methods have been extended to mixed effects models and generalized linear models, but there exist intrinsic drawbacks and limitations. This paper presents two-dimensional plots to identify discordant subjects and observations in generalized linear mixed effects models, displaying discordance in two directions. The sTudentized Residual Sum of Squares is not an extension of any regression tools but a new approach designed to efficiently reflect the characteristics of repeated measures. And this noteworthy clustering of outliers is identified in the plot. Applications to real-life examples are presented to illustrate the favorable/beneficial performance of the new tool.