Marginal longitudinal semiparametric regression via penalized splines

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models

Evaluation of an energy efficiency program in a regional context

The Central Victoria Solar City (CVSC) research trial is part of the Australian Government’s $94 million Solar Cities program. Managed by renewable energy company, Sustainable Regional Australia (SRA), the program encourages residents to test energy efficiency technologies and services designed to reduce energy use and reliance on non-renewable energy sources. The trial involves collecting data from over 3,500 households (including a control group of 750) across central Victoria and recording changes to their energy consumption until 30 June 2013. CVSC is in its early stages of implementation, with about one third of participants recruited. In energy program evaluations,much of the data is hierarchical in nature (e.g. household energy readings over time). An issue with such data is that conventional statistical methods (e.g. OLS or Logistic regression) assumeindependency between observations, which is likely to be violated by longitudinal data. Techniques to address this problem have been a major area of research during the past 10 years. Such developments have led to analytical tools (e.g. Linear Mixed Models), which allow for modeling of dependencies between measures. Early analysis has confirmed the hierarchical nature of the data, with 76% of the variance in pre-program energy consumption occurring between (rather than within) households. A preliminary baseline model based on regional, climatic and household characteristics explains 40% of variation in household electricity consumption. Initial findings suggest that household electricity consumption is most strongly influenced by regional factors (e.g. climate, reticulated gas availability), number of occupants, house size and income

Marginal longitudinal semiparametric regression via penalized splines

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models

Marginal longitudinal semiparametric regression via penalized splines

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models

Impact of COVID-19 pandemic on the Jordanian eating and nutritional habits

Since the emergence of the COVID-19 pandemic, variable measures have been implemented to control the invasion of SARS-CoV-19 worldwide. Some of these measures included lockdowns for several months in some countries. In Jordan, various protocols have been implemented to deal with the epidemic, such as border closures, closures and local protocols for open days until we reach the partial opening in mid-2021. These measures and protocols have affected all sectors in the country, including the education system and the economy. In addition, lifestyle is one of the measurement issues that have been affected by government regulations during the COVID-19 pandemic. In this study, we aimed to investigate the impact of the COVID-19 pandemic on the eating and nutrition habits of the Jordanian population. An online sectional survey was built using Google Forms from Google. Responses were collected from November 12, 2020, through November 24, 2020. Researchers submitted a total of 2,511 responses. The results showed that there was a significant decrease in sugar intake (P = \u3c0.005) with no critical fat and oil intake (P = 0.12). There was a significant change in daily consumption of fruits, vegetables, garlic, onions, and ginger, and downloads of health-related applications and supplements (P \u3c 0.005). In addition, there was an increase in appetite, weight and number of daily meals. These changes are attributed to the extra free time due to lockdown and studying or working from home has significant impacts. However, 31.4% of respondents reported that the limited economic availability of food products and the closure of restaurants and cafes lead to a healthier lifestyle. Only 26.2% have been influenced by family members, friends, doctors, or social media to change their eating habits during the pandemic