arXiv subjects

Ute Hahn

Publications and source records attributed to Ute Hahn.

4 recordsLinked to original sources

arXiv2021

Semiparametric point process modeling of blinking artifacts in PALM

Photoactivated localization microscopy (PALM) is a powerful imaging technique for characterization of protein organization in biological cells. Due to the stochastic blinking of fluorescent probes, and camera discretization effects, each protein gives rise to a cluster of artificial observations. These blinking artifacts are an obstacle for quantitative analysis of PALM data, and tools for their correction are in high demand. We develop the Independent Blinking Cluster point process (IBCpp) family of models, which is suited for modeling of data from single-molecule localization microscopy modalities, and we present results on the mark correlation function. We then construct the PALM-IBCpp - a semiparametric IBCpp tailored for PALM data, and we describe a procedure for estimation of parameters, which can be used without parametric assumptions on the spatial organization of proteins. Our model is validated on nuclear pore complex reference data, where the ground truth was accurately recovered, and we demonstrate how the estimated blinking parameters can be used to perform a blinking corrected test for protein clustering in a cell expressing the adaptor protein LAT. Finally, we consider simulations with varying degrees of blinking and protein clustering to shed light on the expected performance in a range of realistic settings.

stat.AP↗

arXiv2016

A one-way ANOVA test for functional data with graphical interpretation

A new functional ANOVA test, with a graphical interpretation of the result, is presented. The test is an extension of the global envelope test introduced by Myllymaki et al. (2017, Global envelope tests for spatial processes, J. R. Statist. Soc. B 79, 381--404, doi: 10.1111/rssb.12172). The graphical interpretation is realized by a global envelope which is drawn jointly for all samples of functions. If a mean function computed from the empirical data is out of the given envelope, the null hypothesis is rejected with the predetermined significance level $\alpha$. The advantages of the proposed one-way functional ANOVA are that it identifies the domains of the functions which are responsible for the potential rejection. We introduce two versions of this test: the first gives a graphical interpretation of the test results in the original space of the functions and the second immediately offers a post-hoc test by identifying the significant pair-wise differences between groups. The proposed tests rely on discretization of the functions, therefore the tests are also applicable in the multidimensional ANOVA problem. In the empirical part of the article, we demonstrate the use of the method by analyzing fiscal decentralization in European countries. The aim of the empirical analysis is to capture differences between the levels of government expenditure decentralization ratio among different groups of European countries. The idea behind, based on the existing literature, is straightforward: countries with a longer European integration history are supposed to decentralize more of their government expenditure. We use the government expenditure centralization ratios of 29 European Union and EFTA countries in period from 1995 to 2016 sorted into three groups according to the presumed level of European economic and political integration.

stat.ME↗

arXiv2015

Global envelope tests for spatial processes

Envelope tests are a popular tool in spatial statistics, where they are used in goodness-of-fit testing. These tests graphically compare an empirical function $T(r)$ with its simulated counterparts from the null model. However, the type I error probability $α$ is conventionally controlled for a fixed distance $r$ only, whereas the functions are inspected on an interval of distances $I$. In this study, we propose two approaches related to Barnard's Monte Carlo test for building global envelope tests on $I$:(1) ordering the empirical and simulated functions based on their $r$-wise ranks among each other, and (2) the construction of envelopes for a deviation test. These new tests allow the a priori selection of the global $α$ and they yield $p$-values. We illustrate these tests using simulated and real point pattern data.

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arXiv2015

Multiple Monte Carlo Testing with Applications in Spatial Point Processes

The rank envelope test (Myllymäki et al., Global envelope tests for spatial processes, arXiv:1307.0239 [stat.ME]) is proposed as a solution to multiple testing problem for Monte Carlo tests. Three different situations are recognized: 1) a few univariate Monte Carlo tests, 2) a Monte Carlo test with a function as the test statistic, 3) several Monte Carlo tests with functions as test statistics. The rank test has correct (global) type I error in each case and it is accompanied with a $p$-value and with a graphical interpretation which shows which subtest or which distances of the used test function(s) lead to the rejection at the prescribed significance level of the test. Examples of null hypothesis from point process and random set statistics are used to demonstrate the strength of the rank envelope test. The examples include goodness-of-fit test with several test functions, goodness-of-fit test for one group of point patterns, comparison of several groups of point patterns, test of dependence of components in a multi-type point pattern, and test of Boolean assumption for random closed sets.

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