Tero LähderantaUniversity of Oulu · Department of Mathematical Sciences
Tero Lähderanta
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Publications
Publications (13)
Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number...
Zero-inflated explanatory variables, as opposed to outcome variables, are common, for example, in environmental sciences. In this article, we address the problem of having excess of zero values in some continuous explanatory variables, which are subject to multi-outcome lasso-regularized variable selection. In short, the problem results from the fa...
Lasso is a popular and efficient approach to simultaneous estimation and variable selection in high-dimensional regression models. In this paper, a robust LAD-lasso method for multiple outcomes is presented that addresses the challenges of non-normal outcome distributions and outlying observations. Measured covariate data from space or time, or spe...
Using unique administrative register data, we investigate old‐age retirement under the statutory pension scheme in Finland. The analysis is based on multi‐outcome modelling of pensions and working lives together with a range of explanatory variables. An adaptive multi‐outcome LAD‐lasso regression method is applied to obtain estimates of earnings an...
Zero-inflated explanatory variables are common in fields such as ecology and finance. In this paper we address the problem of having excess of zero values in some explanatory variables which are subject to multioutcome lasso-regularized variable selection. Briefly, the problem results from the failure of the lasso-type of shrinkage methods to recog...
Location-allocation and partitional spatial clustering both deal with spatial data, seemingly from different viewpoints. Partitional clustering analyses data points by partitioning them into separate groups, while location-allocation places facilities in locations that best meet the needs of demand points. However, both partitional clustering and l...
Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing, classifying, logistic optimization and infrastructure optimization. Depending on the application at hand, a wide set of extensions may be necessary in clustering.
In this article we propose a number of novel extensions...
The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements...
Spatiotemporal 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-te...
Background:
In the past two decades, the number of maternity hospitals in Finland has been reduced from 42 to 22. Notwithstanding the benefits of centralization for larger units in terms of increased safety, the closures will inevitably impair geographical accessibility of services.
Methods:
This study aimed to employ a set of location-allocatio...
In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Expe...
Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environment is addressed by introduci...