Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles : a simulation study

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis-via simulation studies. Real data on contact wrestling are used for illustration.

An Analysis of the Telecommunications Business in China by Linear Regression

In this paper, we study the influence of the National Telecom Business Volume by the data in 2008 that have been published in China Statistical Yearbook of Statistics. We illustrate the procedure of modeling “National Telecom Business Volume” on the following eight variables, GDP, Consumption Levels, Retail Sales of Social Consumer Goods Total Renovation Investment, the Local Telephone Exchange Capacity, Mobile Telephone Exchange Capacity, Mobile Phone End Users, and the Local Telephone End Users. The testing of heteroscedasticity and multicollinearity for model evaluation is included. We also consider AIC and BIC criterion to select independent variables, and conclude the result of the factors which are the optimal regression model for the amount of telecommunications business and the relation between independent variables and dependent variable. Based on the final results, we propose several recommendations about how to improve telecommunication services and promote the economic development.

Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups

This paper presents a two-step pseudo likelihood estimation technique for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study. An application of it with a binary response variable is presented using a real data set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional pseudo likelihood algorithms in terms of computational time.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Inflammation, oxidative stress, glomerular filtration rate, and albuminuria in elderly men : a cross-sectional study

BACKGROUND: The role of inflammation and oxidative stress in mild renal impairment in the elderly is not well studied. Accordingly, we aimed at investigating the associations between estimated glomerular filtration rate (eGFR), albumin/creatinine ratio (ACR), and markers of different inflammatory pathways and oxidative stress in a community based cohort of elderly men. FINDINGS: Cystatin C-based GFR, ACR, and biomarkers of cytokine-mediated inflammation (interleukin-6, high-sensitivity C-reactive protein[CRP], serum amyloid A[SAA]), cyclooxygenase-mediated inflammation (urinary prostaglandin F2alpha [PGF2alpha]), and oxidative stress (urinary F2 isoprostanes) were assessed in the Uppsala Longitudinal Study of Adult Men(n = 647, mean age 77 years). RESULTS: In linear regression models adjusting for age, BMI, smoking, blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, and treatment with statins, ACE-inhibitors, ASA, and anti-inflammatory agents, eGFR was inversely associated with CRP, interleukin-6, and SAA (beta-coefficient -0.13 to -0.19, p < 0.001 for all), and positively associated with urinary F2-isoprostanes (beta-coefficient 0.09, p = 0.02). In line with this, ACR was positively associated with CRP, interleukin-6, and SAA (beta- coefficient 0.09-0.12, p < 0.02 for all), and negatively associated with urinary F2-isoprostanes (beta-coefficient -0.12, p = 0.002). The associations were similar but with lower regression coefficients in a sub-sample with normal eGFR (>60 ml/min/1.73 m2, n = 514), with the exception that F2-isoprostane and SAA were no longer associated with eGFR. CONCLUSION: Our data indicate that cytokine-mediated inflammation is involved in the early stages of impaired kidney function in the elderly, but that cyclooxygenase-mediated inflammation does not play a role at this stage. The unexpected association between higher eGFR/lower albuminuria and increased F2-isoprostanes in urine merits further studies.

Sysselsättningseffekter i svenska aktiebolag av införandet av RUT-avdraget : En Difference-in-Differenceanalys

Detta arbete har gjorts med syftet att utvärdera sysselsättningseffekterna i svenska aktiebolag av införandet av RUT-avdraget. RUT-avdraget infördes 2007 och innebär att privatpersoner kan få göra skattereduktion för olika typer av hushållsarbeten. Datamaterialet som används i denna studie är bokföringsdata för alla Sveriges aktiebolag mellan 2000 – 2010, aggregerat till tresiffriga SNI-koder för alla de svenska kommunerna. Utifrån datamaterialet har RUT-avdragets sysselsättningseffekter analyserats med hjälp av en Difference-in-Differencemodell. Resultatet visar att RUT-avdraget gjort att 6930 nya arbeten har skapats i de svenska aktiebolag som ingår i RUT-sektorn. Detta innebär alltså att RUT-avdraget har haft en positiv effekt på sysselsättningen.

Factors facilitating a national quality registry to aid clinical quality improvement : findings of a national survey

Objectives: While national quality registries (NQRs) are suggested to provide opportunities for systematic follow-up and learning opportunities, and thus clinical improvements, features in registries and contexts triggering such processes are not fully known. This study focuses on one of the world's largest stroke registries, the Swedish NQR Riksstroke, investigating what aspects of the registry and healthcare organisations facilitate or hinder the use of registry data in clinical quality improvement. Methods: Following particular qualitative studies, we performed a quantitative survey in an exploratory sequential design. The survey, including 50 items on context, processes and the registry, was sent to managers, physicians and nurses engaged in Riksstroke in all 72 Swedish stroke units. Altogether, 242 individuals were presented with the survey; 163 responded, representing all but two units. Data were analysed descriptively and through multiple linear regression. Results: A majority (88%) considered Riksstroke data to facilitate detection of stroke care improvement needs and acknowledged that their data motivated quality improvements (78%). The use of Riksstroke for quality improvement initiatives was associated (R2=0.76) with ‘Colleagues’ call for local results’ (p=<0.001), ‘Management Request of Registry data’ (p=<0.001), and it was said to be ‘Simple to explain the results to colleagues’ (p=0.02). Using stepwise regression, ‘Colleagues’ call for local results’ was identified as the most influential factor. Yet, while 73% reported that managers request registry data, only 39% reported that their colleagues call for the unit's Riksstroke results. Conclusions: While an NQR like Riksstroke demonstrates improvement needs and motivates stakeholders to make progress, local stroke care staff and managers need to engage to keep the momentum going in terms of applying registry data when planning, performing and evaluating quality initiatives.