Exploring an analytic model of urban housing strata

An analytic model of urban housing strata is developed which utilizes housing structure type, housing tenure type, floor size, physical quality, residential area, and number of rooms to calculate a housing deficit for each housing characteristic. The housing norm is subtracted from the actual housing conditions. Each housing deficit value is weighted according to the priority of the 6 variables and then summed as the housing strata score. Negative scores are below the norm and positive ones above. The model is applied to empirical data for Seoul, Korea. The findings were that 66% of the family sample showed negative scores (unsatisfactory housing conditions). Scores range from -22 to =or+ 18. Morris and Winter's "housing adjustment model" is used to explain housing behavior when there is a gap between housing conditions and the norm. Housing behavior is analyzed with multiple regression analysis of housing strata, social strata, and family life stage variables. Findings indicate that the establishment stage in the family life cycle is more likely to be associated with upper housing strata. From the way the model is set up only those in the establishment, childbearing, and child-rearing stage could get a positive deficit housing score. Size of household is not statistically significant, but upper housing strata are reflective of families with 2.5 members. Those with 3-4.5 members may be in the upper middle housing strata. Those with 5 children are in the lower middle housing strata. Housing strata are significantly related to housing structure type, tenure type, and size and number of rooms. The high rise apartment is likely to be in the upper, the row house and multifamily house in the lower housing, and the single detached house is distributed through all 4 strata. Home ownership is highest in the upper strata. The proportion of housing with 18 pyong and 2 rooms is higher in the lower strata, while housing with 19-32 pyong (63-106 sq. ms) and 3 rooms is higher in the middle housing strata. Housing satisfaction is significantly explained by housing strata but not general social strata (r = .13). Propensity to move is explained by family life stage followed by housing strata.     

Relationship building and the use of Web sites: How Fortune 500 corporations use their Web sites to build relationships

This study examines the dialogic features of corporate Web sites in order to determine the Web site practices of the corporations for building relationships with their publics. Content analysis of 100 Fortune 500 companies’ Web sites revealed that the corporations designed their Web sites to serve important publics and foster dialogic communication. The corporate Web sites appear to promote control mutuality, trust, satisfaction, openness, and intimacy. However, the corporations need to maintain repetitive interactions with their publics to enhance trust, commitment, and exchange relationship.     

Relative error prediction via kernel regression smoothers

In this article, we introduce and study local constant and local linear nonparametric regression estimators when it is appropriate to assess performance in terms of mean squared relative error of prediction. We give asymptotic results for both boundary and non-boundary cases. These are special cases of more general asymptotic results that we provide concerning the estimation of the ratio of conditional expectations of two functions of the response variable. We also provide a good bandwidth selection method for the estimators. Examples of application, limited simulation results and discussion of related problems and approaches are also given.     

Structured kernel quantile regression

Quantile regression can provide more useful information on the conditional distribution of a response variable given covariates while classical regression provides informations on the conditional mean alone. In this paper, we propose a structured quantile estimation methodology in a nonparametric function estimation setup. Through the functional analysis of variance decomposition, the optimization of the proposed method can be solved using a series of quadratic and linear programmings. Our method automatically selects relevant covariates by adopting a lasso-type penalty. The performance of the proposed methodology is illustrated through numerical examples on both simulated and real data.     

A Bayesian approach for improved pavement performance prediction

We present a method for predicting future pavement distresses such as longitudinal cracking. These predicted distress values are used to plan road repairs. Large inherent variability in measured cracking and an extremely small number of observations are the nature of the pavement cracking data, which calls for a parametric Bayesian approach. We model theoretical pavement distress with a sigmoidal equation with coefficients based on prior engineering knowledge. We show that a Bayesian formulation akin to Kalman filtering gives sensible predictions and provides defendable uncertainty statements for predictions. The method is demonstrated on data collected by the Texas Transportation Institute at several sites in Texas. The predictions behave in a reasonable and statistically valid manner.     

A test for multivariate structure

We present a test for detecting 'multivariate structure' in data sets. This procedure consists of transforming the data to remove the correlations, then discretizing the data and, finally, studying the cell counts in the resulting contingency table. A formal test can be performed using the usual chi-squared test statistic. We give the limiting distribution of the chi-squared statistic and also present simulation results to examine the accuracy of this limiting distribution in finite samples. Several examples show that our procedure can detect a variety of different types of structure. Our examples include data with clustering, digitized speech data, and residuals from a fitted time series model. The chi-squared statistic can also be used as a test for multivariate normality.     

Goodness-of-fit test for uniform stochastic ordering among several distributions

The likelihood-ratio test (LRT) is considered as a goodness-of-fit test for the null hypothesis that several distribution functions are uniformly stochastically ordered. Under the null hypothesis, H₁ : F₁ ? F₂ ?···? F_N, the asymptotic distribution of the LRT statistic is a convolution of several chi-bar-square distributions each of which depends upon the location parameter. The least-favourable parameter configuration for the LRT is not unique. It can be two different types and depends on the number of distributions, the number of intervals and the significance level α. This testing method is illustrated with a data set of survival times of five groups of male fruit flies.     

The mixed model for survey regression estimation

Estimation of the population mean under the regression model with random components is considered. Conditions under which the random components regression estimator is design consistent are given. It is shown that consistency holds when incorrect values are used for the variance components. The regression estimator constructed with model parameters that differ considerably from the true parameters performed well in a Monte Carlo study. Variance estimators for the regression predictor are suggested. A variance estimator appropriate for estimators constructed with a biased estimator for the between-group variance component performed well in the Monte Carlo study.     

Censored time series analysis with autoregressive moving average models

The authors consider time series observations with data irregularities such as censoring due to a detection limit. Practitioners commonly disregard censored data cases which often result in biased estimates. The authors present an attractive remedy for handling autocorrelated censored data based on a class of autoregressive and moving average (ARMA) models. In particular, they introduce an imputation method well suited for fitting ARMA models in the presence of censored data. They demonstrate the effectiveness of their technique in terms of bias, efficiency, and information loss. They also describe its adaptation to a specific context of meteorological time series data on cloud ceiling height, which are measured subject to the detection limit of the recording device.