2,158 research outputs found
P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data
The P-splines of Eilers and Marx (1996) combine a B-spline basis with a
discrete quadratic penalty on the basis coefficients, to produce a reduced rank
spline like smoother. P-splines have three properties that make them very
popular as reduced rank smoothers: i) the basis and the penalty are sparse,
enabling efficient computation, especially for Bayesian stochastic simulation;
ii) it is possible to flexibly `mix-and-match' the order of B-spline basis and
penalty, rather than the order of penalty controlling the order of the basis as
in spline smoothing; iii) it is very easy to set up the B-spline basis
functions and penalties. The discrete penalties are somewhat less interpretable
in terms of function shape than the traditional derivative based spline
penalties, but tend towards penalties proportional to traditional spline
penalties in the limit of large basis size. However part of the point of
P-splines is not to use a large basis size. In addition the spline basis
functions arise from solving functional optimization problems involving
derivative based penalties, so moving to discrete penalties for smoothing may
not always be desirable. The purpose of this note is to point out that the
three properties of basis-penalty sparsity, mix-and-match penalization and ease
of setup are readily obtainable with B-splines subject to derivative based
penalization. The penalty setup typically requires a few lines of code, rather
than the two lines typically required for P-splines, but this one off
disadvantage seems to be the only one associated with using derivative based
penalties. As an example application, it is shown how basis-penalty sparsity
enables efficient computation with tensor product smoothers of scattered data
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors
Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information
Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability
Estimated Acute Effects of Ambient Ozone and Nitrogen Dioxide on Mortality in the Pearl River Delta of Southern China
Background and objectives: Epidemiologic studies have attributed adverse health effects to air pollution; however, controversy remains regarding the relationship between ambient oxidants [ozone (O3) and nitrogen dioxide (NO2)] and mortality, especially in Asia. We conducted a four-city time-series study to investigate acute effects of O3 and NO2 in the Pearl River Delta (PRD) of southern China, using data from 2006 through 2008
Modelling a response as a function of high frequency count data: the association between physical activity and fat mass
We present a new statistical modelling approach where the response is a
function of high frequency count data. Our application is about investigating
the relationship between the health outcome fat mass and physical activity (PA)
measured by accelerometer. The accelerometer quantifies the intensity of
physical activity as counts per epoch over a given period of time. We use data
from the Avon longitudinal study of parents and children (ALSPAC) where
accelerometer data is available as a time series of accelerometer counts per
minute over seven days for a subset of children. In order to compare
accelerometer profiles between individuals and to reduce the high dimension a
functional summary of the profiles is used. We use the histogram as a
functional summary due to its simplicity, suitability and ease of
interpretation. Our model is an extension of generalised regression of scalars
on functions or signal regression. It allows also multi-dimensional functional
predictors and additive non-linear predictors for metric covariates. The
additive multidimensional functional predictors allow investigating specific
questions about whether the effect of PA varies over its intensity, by gender,
by time of day or by day of the week. The key feature of the model is that it
utilises the full profile of measured PA without requiring cut-points defining
intensity levels for light, moderate and vigorous activity. We show that the
(not necessarily causal) effect of PA is not linear and not constant over the
activity intensity. Also, there is little evidence to suggest that the effect
of PA intensity varies by gender or whether it happens on weekdays or on
weekends
Attitudes towards the use and acceptance of eHealth technologies : a case study of older adults living with chronic pain and implications for rural healthcare
Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1. MC’s time writing the paper is funded by the Scottish Government’s Rural and Environmental Science and Analytical Services Division (RESAS) under Theme 8 ‘Vibrant Rural Communities’ of the Food, Land and People Programme (2011–2016). MC is also an Honorary Research Fellow at the Division of Applied Health Sciences, University of Aberdeen. The input of other members of the TOPS research team, Alastair Mort, Fiona Williams, Sophie Corbett, Phil Wilson and Paul MacNamee who contributed to be wider study and discussed preliminary findings reported here with the authors of the paper is acknowledged. We acknowledge the feedback on earlier versions of this paper provided by members of the Trans-Atlantic Rural Research Network, especially Stefanie Doebler and Carmen Hubbard. We also thank Deb Roberts for her comments.Peer reviewedPublisher PD
Habitat Selection and Temporal Abundance Fluctuations of Demersal Cartilaginous Species in the Aegean Sea (Eastern Mediterranean)
Predicting the occurrence of keystone top predators in a multispecies marine environment, such as the Mediterranean Sea, can be of considerable value to the long-term sustainable development of the fishing industry and to the protection of biodiversity. We analysed fisheries independent scientific bottom trawl survey data of two of the most abundant cartilaginous fish species (Scyliorhinus canicula, Raja clavata) in the Aegean Sea covering an 11-year sampling period. The current findings revealed a declining trend in R. clavata and S. canicula abundance from the late ′90 s until 2004. Habitats with the higher probability of finding cartilaginous fish present were those located in intermediate waters (depth: 200–400 m). The present results also indicated a preferential species' clustering in specific geographic and bathymetric regions of the Aegean Sea. Depth appeared to be one of the key determining factors for the selection of habitats for all species examined. With cartilaginous fish species being among the more biologically sensitive fish species taken in European marine fisheries, our findings, which are based on a standardized scientific survey, can contribute to the rational exploitation and management of their stocks by providing important information on temporal abundance trends and habitat preferences
- …