DISA

When? November 27, 14.00-16.00
Where? Online, https://lnu-se.zoom.us/j/61812638609?pwd=zy5Z2oFW3lhVh3C0bqYAqBJFpRNwnm.1&from=addon

Abstracts

Mahalanobis Distance and Angle under a SCAD-Dual Modified-Cholesky Precision Estimator: The Gaussian case – Deliang Dai from Department of Economics and Statistics
We study finite-sample and asymptotic properties of Mahalanobis distances and angles computed with a row-separable modified-Cholesky precision estimator that combines SCAD selection and a positive-semidefinite quadratic penalty. For an independent Gaussian observation, we give the exact conditional generalized-chi-square distribution of the estimated squared distance, a finite-sample deviation inequality, and a central limit theorem on the $\sqrt{2p}$ scale. For the Mahalanobis cosine between two deterministic vectors, we obtain a finite-sample perturbation bound and an asymptotic normal approximation with an explicit direction-dependent Gaussian quadratic-form variance. Separate conditions permit centering at the true cosine and feasible studentization.  The estimator is defined by two local-linear-approximation updates, whose agreement with an oracle path is established under explicit initialization, signal-separation and tuning assumptions. Simulation and empirical studies are presented to evaluate the theoretical results. 

Nonlinear forecasting with many predictors using mixed data sampling kernel ridge regression models – Kristofer Månsson from Department of Economics and Statistics
Policy institutes such as central banks need accurate forecasts of key measures of economic activity to design stabilization policies that reduce the severity of economic fluctuations. Therefore, this paper develops a kernel ridge regression estimator in a mixed data sampling framework. Kernel ridge regression can handle many predictors with a nonlinear relationship to the target variable. Consequently, it has potential to improve the currently used principal component-based methods when the economic data follow a nonlinear factor structure. In a Monte Carlo study, we show that the kernel ridge regression approach is superior in terms of mean square error and is more robust than principal component-based methods to different nonlinear data generating processes. By using a dataset consisting of 24 economic indicators, we forecast Swedish gross domestic production. The results confirm the superiority of the kernel ridge regression approach. Therefore, we suggest that policy institutes consider the use of kernel-based approaches when forecasting key measures of economic activity.

We have an exciting few weeks ahead, with no fewer than three public PhD defenses on the horizon. We warmly invite you to join us for these important academic events and celebrate the accomplishments of our doctoral candidates. 
Farid Edrisi, 17 November  
On 17 November at 1:00 p.m., Farid will defend his doctoral thesis, Engineering Proactive Self-Adaptation in Cyber-Physical Systems under Uncertainty. The defense will take place in room N1017, N Building, at Linnaeus University in Växjö. 

Dag Björnberg, 20 November  
On 20 November, at 9:30 a.m., Dag, who combined his doctoral studies with his role at Softwerk through our industrial graduate school, DIA, will defend his thesis Machine Learning under Data Scarcity: Methods and Applications in Forestry. The public defense will take place in Newton, Building C, at Linnaeus University in Växjö. 

Rakhshanda Jabeen, 4 December   
On 4 December at 9:15 a.m., Rakhshanda, who pursued her doctoral studies in collaboration with Electrolux within DIA, will defend her thesis Representing Architectural Evidence in Software Systems: A Graph Learning Approach. The defense will take place in Room D1136, Building D, at Linnaeus University in Växjö.