arXiv subjects

Tero Lähderanta

Publications and source records attributed to Tero Lähderanta.

4 recordsLinked to original sources

arXiv2022

Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso

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 spectral bands or genomic positions often have natural correlation structure arising from measuring distance between the covariates. The proposed multi-outcome approach includes handling of such covariate blocks by a group fusion penalty, which encourages similarity between neighboring regression coefficient vectors by penalizing their differences for example in sequential data situation. Properties of the proposed approach are first illustrated by extensive simulations, and secondly the method is applied to a real-life skewed data example on retirement behavior with heteroscedastic explanatory variables.

stat.ME↗

arXiv2021

Reducing bias and alleviating the influence of excess of zeros with multioutcome adaptive LAD-lasso

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 recognize any difference between zero value occurring either in the regression coefficient or in the corresponding value of the explanatory variable. This kind of confounding will obviously increase number of false positives - all non-zero regression coefficients do not necessarily represent real outcome effects. We present here the adaptive LAD-lasso for multiple outcomes which extends the earlier work of multivariate LAD-lasso with adaptive penalization. In addition of well known property of having less biased regression coefficients, we show here how the adaptivity improves also method's ability to recover from influences of excess of zero values measured in continuous covariates.

stat.ME↗

arXiv2021

Edge computing server placement with capacitated location allocation

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 for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location-allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

cs.NI↗

arXiv2021

Capacitated spatial clustering with multiple constraints and attributes

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 to PACK that is a novel capacitated spatial clustering method. These extensions are relocation and location preference of cluster centers, outliers, and non-spatial attributes. The strength of PACK is that it can consider all of these extensions jointly. We demonstrate the usefulness PACK with a real world example in edge computing server placement for a city region with various different set ups, where we take into consideration outliers, center placement, and non-spatial attributes. Different setups are evaluated with summary statistics on spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved at best by 53%, while simultaneously the proximity degraded only 18%. In alternate scenarios, both proximity and similarity were improved. The different extensions proved to provide a valuable way to include non-spatial information into the cluster analysis, and attain better overall proximity and similarity. Furthermore, we provide easy-to-use software tools (rpack) for conducting clustering analyses.

stat.AP↗