LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

A scale space multiresolution feature extraction method is proposed for time series data. The method detects intervals where time series features differ from their surroundings, and it produces a multiresolution analysis of the series as a sum of scale‐dependent components. These components are obtained from differences of smooths. The relevant sequence of smoothing levels is determined using derivatives of smooths with respect to the logarithm of the smoothing parameter. As time series are usually noisy, the method uses Bayesian inference to establish the credibility of the components. © The Authors. Stat published by John Wiley & Sons Ltd.

In Finnish Lapland, reindeer herders’ activity is strongly dependent on the surrounding natural environment, which is directly exposed to environmental changes and climatic variations. By assessing whether there is any evidence of change in climate in Fell Lapland over the last 50 years, this paper attempts to link global climatic trends with local conditions and respond to the need of information at the local level. It aims at assessing the changes in temperature, precipitation and snow cover at a regional and local scale, as well as determining the climatic trends for the period 1960–2011. Statistical methods were used to conduct analyses of the regional homogeneity, the annual and seasonal variability, and the cold intensity. The results show that the regional climate is not homogeneous and differences exist between locations. Nevertheless, it can be concluded that, in general, a warming trend is discernible for the period 1960–2011, frost and thaw cycles slightly increase, and variations in mean temperatures are more important in the winter. Precipitation is more variable according to the site but, in general, precipitation is increasing with time, especially in the winter, and the snow cover does not seem to contain any discernible trend.

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.

This article considers the detection of image features in different spatial scales. The main focus is on capturing the scale-dependent differences in a pair of noisy images, but the technique developed can also be applied to the analysis of single images. The approach proposed uses Bayesian statistical modeling and simulation-based inference, and it can be viewed as a further development of SiZer technology, originally designed for nonparametric curve fitting. Numerical examples include artificial test images and a preliminary analysis of a pair of Landsat images used in satellite-based forest inventory. This article has supplementary material online.

Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstructions, and Bayesian inference to study changes in forests. Using stereopairs of aerial photographs from five boreal forest landscapes, we visually interpreted canopy cover in contiguous 0.1-ha cells at three time points during 1959–2011. We used tree-ring measurements to produce calibration data for the interpretation, and to quantify the bias and error associated with the interpretation. Then, we discerned credible canopy cover changes from the interpretation error noise using Bayesian inference. We underestimated canopy cover using the historical low-quality photographs, and overestimated it using the recent high-quality photographs. Further, due to differences in tree species composition and canopy cover in the cells, the interpretation bias varied between the landscapes. In addition, the random interpretation error varied between and within the landscapes. Due to the varying bias and error, the magnitude of credibly detectable canopy cover change in the 0.1-ha cells depended on the studied time interval and landscape, ranging from −10 to −18 percentage points (decrease), and from +10 to +19 percentage points (increase). Hence, changes occurring at stand scales were detectable, but smaller scale changes could not be separated from the error noise. Besides the abrupt changes, also slow continuous canopy cover changes could be detected with the proposed approach. Given the wide availability of historical aerial photographs, the proposed approach can be applied for forest change analysis in biomes where tree-rings form, while accounting for the bias and error in aerial photo interpretation.