A homoscedasticity test for the accelerated failure time model

The semiparametric accelerated failure time (AFT) model is a popular linear model in survival analysis. AFT model and its associated inference methods assume homoscedasticity of the survival data. It is shown that violation of this assumption will lead to inefficient parameter estimation and anti-conservative confidence interval estimation, and thus, misleading conclusions in survival data analysis. However, there is no valid statistical test proposed to test the homoscedasticity assumption. In this paper, we propose the first novel quasi-likelihood ratio test for the homoscedasticity assumption in the AFT model. Simulation studies show the test performs well. A real dataset is used to demonstrate the usefulness of the developed test

Quasi-likelihood Ratio Tests for Homoscedasticity of Variance in Linear Regression

Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests

GAMLSS for high-dimensional data – a flexible approach based on boosting

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs

Structural Nested Models and G-estimation: The Partially Realized Promise

Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures. The models and estimation methods have since been extended to dealing with a broader series of problems, and have considerable advantages over the other methods developed for estimating such joint effects. Despite these advantages, the application of these methods in applied research has been relatively infrequent; we view this as unfortunate. To remedy this, we provide an overview of the models and estimation methods as developed, primarily by Robins, over the years. We provide insight into their advantages over other methods, and consider some possible reasons for failure of the methods to be more broadly adopted, as well as possible remedies. Finally, we consider several extensions of the standard models and estimation methods.Comment: Published in at http://dx.doi.org/10.1214/14-STS493 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Statistical Tools for Analyzing Water Quality Data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm

Land Use Change and Ecosystem Valuation in North Georgia

A model of land allocation at the aggregate watershed level was developed assuming profit/net benefit maximization under risk neutrality. The econometric land use model was analyzed as an equation by equation SURE model as all the independent variables were the same for both equations. In analyzing effect of land use change on water quality, we took year 2005 as our baseline and postulated three land use scenarios. We applied Benefit Transfer techniques to value water quality changes resulting from land use change and estimated lower bounds for WTP to improve water quality to meet the FCB criterion for drinking water supply and fishing waters and BOD (DO) criteria for fishing waters. Water quality modeling revealed that land use change would result in increased runoff, and associated increase in FCB and BOD/DO violations. But the BOD/DO violations could be curtailed by managing urban growth as evidenced absence of BOD violations in the managed growth scenario. Our study finds there may be problems of FCB under all postulated future land use scenarios. The findings also support existing literature that there are problems with FCB violation in the study area at the moment. Finally, it seems that the people of UCRB would be willing to pay a lower bound value between USD 15,785,740 and USD 16,141,230 per year to create and maintain quality standards for fishing and drinking water supply.Ecosystem, Economic value, North Georgia, land use, land use change, fish, water quality, structural time series, willingness to pay, benefit transfer, forecasting, vector autoregression, Upper Chattahoochee River, Environmental Economics and Policy, Land Economics/Use,

The role of negative mood states and consequences of hypersexual behaviours in predicting hypersexuality among university students

Background and Aims: The issue of whether hypersexual behaviours exist among university students is controversial because many of these individuals engage in sexual exploration during their time at university. To date, little is known about the correlates of hypersexual behaviours among university students in the UK. Therefore, the aims of this exploratory study were two-fold. Firstly, to explore and establish the correlates of hypersexual behaviours, and secondly, to investigate whether hypersexuality among university students can be predicted by variables relating to negative mood states (i.e., emotional dysregulation, loneliness, shame, and life satisfaction) and consequences of hypersexual behaviour. Methods: Survey data from 165 British university students was analysed using regression analyses. Results: The full regression model significantly predicted hypersexual behaviours. However, only a small number of predictor variables (i.e., gender, consequences of hypersexual behaviours, life satisfaction and emotional dysregulation) accounted for the significant unique influence on hypersexual behaviours among the sample. Conclusions: The study empirically supported the concept of hypersexual disorder. The implications of these findings are also discussed

Sunk costs, market contestability, and the size distribution of firms

This paper offers a new economic explanation for the observed inter-industry differences in the size distribution of firms. The empirical estimates--based on three temporal (1982, 1987, and 1992) cross-sections of the four-digit United States manufacturing industries--indicate that increased market contestability, as signified by low sunk costs, tends to reduce the dispersion of firm sizes. These findings provide support for one of the key predictions of the theory of contestable markets: that market forces under contestability would tend to render any inefficient organization of the industry unsustainable and, consequently, tighten the distribution of firms around the optimum.Markets and Market Access,Economic Theory&Research,Water and Industry,Access to Markets,Debt Markets

A single-level random-effects cross-lagged panel model for longitudinal mediation analysis

Cross-lagged panel models (CLPMs) are widely used to test mediation with longitudinal panel data. One major limitation of the CLPMs is that the model effects are assumed to be fixed across individuals. This assumption is likely to be violated (i.e., the model effects are random across individuals) in practice. When this happens, the CLPMs can potentially yield biased parameter estimates and misleading statistical inferences. This article proposes a model named a random-effects cross-lagged panel model (RE-CLPM) to account for random effects in CLPMs. Simulation studies show that the RE-CLPM outperforms the CLPM in recovering the mean indirect and direct effects in a longitudinal mediation analysis when random effects exist in the population. The performance of the RE-CLPM is robust to a certain degree, even when the random effects are not normally distributed. In addition, the RE-CLPM does not produce harmful results when the model effects are in fact fixed in the population. Implications of the simulation studies and potential directions for future research are discussed

Using Large Datasets of Organic Photovoltaic Performance Data to Elucidate Trends in Reliability Between 2009 and 2019

The application of data analytical approaches to understand long-term stability trends of organic photovoltaics (OPVs) is presented. Nearly 1900 OPV data points have been catalogued, and multivariate analysis has been applied in order to identify patterns, produce models that quantitatively compare different internal and external stress factors, and subsequently enable predictions of OPV stability to be achieved. Analysis of the weights associated with the acquired predictive model shows that for light stability (ISOS-L) testing, the most significant factor for increasing the time taken to reach 80% of the initial performance (T80) is the substrate and top electrode selection, and the best light stability is achieved with a small molecule active layer. The weights for damp-heat (ISOS-D) testing shows that the type of encapsulation is the primary factor affecting the degradation to T80. The use of data analytics and potentially machine learning can provide researchers in this area new insights into degradation patterns and emerging trends