19 citations · 33 across the 7 of their papers we have counts for
8 papers
A Formal Graphical Inference Framework for Combining Effect Size with Statistical Significance: Application to Multivariate and Functional Linear Models
Tomáš Mrkvička, Mikko Kuronen, Mari Myllymäki
To address the critical need for statistical methods that evaluate practical magnitude alongside statistical significance, we introduce a formal graphical inference framework that…
Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish
Nhan Phan, Mikko Kuronen, Maria Kautonen +6
Mispronunciation detection (MD) models are the cornerstones of many language learning applications. Unfortunately, most systems are built for English and other major languages, whi…
What you see is not what is there: Mechanisms, models, and methods for point pattern deviations
Peter Guttorp, Janine Illian, Joel Kostensalo +4
Many natural systems are observed as point patterns in time, space, or space and time. Examples include plant and cellular systems, animal colonies, earthquakes, and wildfires. In…
Global quantile regression
Tomáš Mrkvička, Konstantinos Konstantinou, Mikko Kuronen +1
Quantile regression is used to study effects of covariates on a particular quantile of the data distribution. Here we are interested in the question whether a covariate has any eff…
Point process models for sweat gland activation observed with noise
Mikko Kuronen, Mari Myllymäki, Adam Loavenbruck +1
The aim of the paper is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of swe…
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
Mikko Kuronen, Aila Särkkä, Matti Vihola +1
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to invest…
New methods for multiple testing in permutation inference for the general linear model
Tomas Mrkvicka, Mari Myllymaki, Mikko Kuronen +1
Permutation methods are commonly used to test significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimagin…
Hard-core thinnings of germ-grain models with power-law grain sizes
Mikko Kuronen, Lasse Leskelä
Random sets with long-range dependence can be generated using a Boolean model with power-law grain sizes. We study thinnings of such Boolean models which have the hard-core propert…