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Publications (10 of 23) Show all publications
Pya Arnqvist, N., Sjöstedt de Luna, S. & Abramowicz, K. (2024). fiberLD: Fiber Length Determination. R package version 0.1-8.
Open this publication in new window or tab >>fiberLD: Fiber Length Determination. R package version 0.1-8
2024 (English)Other (Other academic)
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-220032 (URN)
Note

Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities.

Available from: 2024-01-26 Created: 2024-01-26 Last updated: 2024-01-26Bibliographically approved
Abramowicz, K., Pini, A., Schelin, L., Sjöstedt de Luna, S., Stamm, A. & Vantini, S. (2023). Domain selection and family-wise error rate for functional data: a unified framework. Biometrics, 79(2), 1119-1132
Open this publication in new window or tab >>Domain selection and family-wise error rate for functional data: a unified framework
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2023 (English)In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 79, no 2, p. 1119-1132Article in journal (Refereed) Published
Abstract [en]

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as component-wise inference for multivariate data naturally performs feature selection, subset-wise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on point-wise evaluations of functional data are suitably adjusted for providing a control of the family-wise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined threshold-wise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a-priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples, and compare their performance with other existing methods.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
adjusted p-value function, functional data, local inference, permutation test
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-193740 (URN)10.1111/biom.13669 (DOI)000788027300001 ()35352337 (PubMedID)2-s2.0-85129057480 (Scopus ID)
Funder
Swedish Research Council, 2016-02763Swedish Research Council, 340-2013-5203
Note

First published online: 30 March 2022

Available from: 2022-04-12 Created: 2022-04-12 Last updated: 2023-09-04Bibliographically approved
Pataky, T. C., Abramowicz, K., Liebl, D., Pini, A., Sjöstedt de Luna, S. & Schelin, L. (2023). Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with interval-wise testing. AStA Advances in Statistical Analysis, 107, 369-392
Open this publication in new window or tab >>Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with interval-wise testing
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2023 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, Vol. 107, p. 369-392Article in journal (Refereed) Published
Abstract [en]

The recent sports science literature conveys a growing interest in robust statistical methods to analyze smooth, regularly-sampled functional data. This paper focuses on the inferential problem of identifying the parts of a functional domain where two population means differ. We considered four approaches recently used in sports science: interval-wise testing (IWT), statistical parametric mapping (SPM), statistical nonparametric mapping (SnPM) and the Benjamini-Hochberg (BH) procedure for false discovery control. We applied these procedures to both six representative sports science datasets, and also to systematically varied simulated datasets which replicated ten signal- and/or noise-relevant parameters that were identified in the experimental datasets. We observed generally higher IWT and BH sensitivity for five of the six experimental datasets. BH was the most sensitive procedure in simulation, but also had relatively high false positive rates (generally > 0.1) which increased sharply (> 0.3) in certain extreme simulation scenarios including highly rough data. SPM and SnPM were more sensitive than IWT in simulation except for (1) high roughness, (2) high nonstationarity, and (3) highly nonuniform smoothness. These results suggest that the optimum procedure is both signal and noise-dependent. We conclude that: (1) BH is most sensitive but also susceptible to high false positive rates, (2) IWT, SPM and SnPM appear to have relatively inconsequential differences in terms of domain identification sensitivity, except in cases of extreme signal/noise characteristics, where IWT appears to be superior at identifying a greater portion of the true signal.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
One-dimensional functional data, Local inference, Continuum data analysis, Simulation, Signal modeling, Kinematics, Biomechanics
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-188526 (URN)10.1007/s10182-021-00418-4 (DOI)000702599300001 ()2-s2.0-85116225860 (Scopus ID)
Funder
Swedish Research Council, 2016-02763Swedish Research Council, 2013-5203
Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2023-07-14Bibliographically approved
Pya Arnqvist, N., Sjöstedt de Luna, S. & Abramowicz, K. (2022). fiberLD: Fiber Length Determination. R package version 0.1-7.
Open this publication in new window or tab >>fiberLD: Fiber Length Determination. R package version 0.1-7
2022 (English)Other (Other academic)
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-198597 (URN)
Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2022-08-23Bibliographically approved
Abramowicz, K., Sjöstedt de Luna, S. & Strandberg, J. (2022). Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data. Computational Statistics & Data Analysis, 177, Article ID 107583.
Open this publication in new window or tab >>Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data
2022 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 177, article id 107583Article in journal (Refereed) Published
Abstract [en]

Nonparametric bagging clustering methods are studied and compared to identify latent structures from a sequence of dependent categorical data observed along a one-dimensional (discrete) time domain. The frequency of the observed categories is assumed to be generated by a (slowly varying) latent signal, according to latent state-specific probability distributions. The bagging clustering methods use random tessellations (partitions) of the time domain and clustering of the category frequencies of the observed data in the tessellation cells to recover the latent signal, within a bagging framework. New and existing ways of generating the tessellations and clustering are discussed and combined into different bagging clustering methods. Edge tessellations and adaptive tessellations are the new proposed ways of forming partitions. Composite methods are also introduced, that are using (automated) decision rules based on entropy measures to choose among the proposed bagging clustering methods. The performance of all the methods is compared in a simulation study. From the simulation study it can be concluded that local and global entropy measures are powerful tools in improving the recovery of the latent signal, both via the adaptive tessellation strategies (local entropy) and in designing composite methods (global entropy). The composite methods are robust and overall improve performance, in particular the composite method using adaptive (edge) tessellations.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Bagging methods, Categorical dependent data, Clustering, Entropy
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-198931 (URN)10.1016/j.csda.2022.107583 (DOI)000930488900007 ()2-s2.0-85135796679 (Scopus ID)
Funder
Swedish Research Council, 340-2013-5203
Available from: 2022-09-19 Created: 2022-09-19 Last updated: 2023-09-05Bibliographically approved
Sjöstedt de Luna, S., Abramowicz, K. & Pya Arnqvist, N. (2021). Non-destructive methods for assessing tree fiber length distributions in standing trees.
Open this publication in new window or tab >>Non-destructive methods for assessing tree fiber length distributions in standing trees
2021 (English)Manuscript (preprint) (Other academic)
Abstract [en]

One of the main concerns of silviculture and forest management focuses on finding fast, cost-efficient and non-destructive ways of measuring wood properties in standing trees. This paper presents an R package \verb+fiberLD+ that provides functions for estimating tree fiber length distributions in the standing tree based on increment core samples. The methods rely on increment core data measured by means of an optical fiber analyzer (OFA) or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibers (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibers. The microscopy measured data consist of the observed lengths of the uncut fibers in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions. Two choices for the assumptions of the underlying density functions of the true fiber (fine) lengths of those fibers (fines) that at least partially appear in the increment core are considered, such as the generalized gamma and the log normal densities. Maximum likelihood estimation is used for estimating the model parameters for both the OFA analyzed data and the microscopy measured data.

Publisher
p. 25
Keywords
fiber length, censoring, increment core, generalized gamma, mixture density
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-187956 (URN)
Available from: 2021-09-28 Created: 2021-09-28 Last updated: 2022-01-17
Pya Arnqvist, N., Sjöstedt de Luna, S. & Abramowicz, K. (2019). fiberLD: Fiber Length Determination. R package version 0.1-6.
Open this publication in new window or tab >>fiberLD: Fiber Length Determination. R package version 0.1-6
2019 (English)Other (Other academic)
National Category
Mathematics
Identifiers
urn:nbn:se:umu:diva-172000 (URN)
Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2020-06-22Bibliographically approved
Abramowicz, K., Schelin, L., Sjöstedt de Luna, S. & Strandberg, J. (2019). Multiresolution clustering of dependent functional data with application to climate reconstruction. Stat, 8(1), Article ID e240.
Open this publication in new window or tab >>Multiresolution clustering of dependent functional data with application to climate reconstruction
2019 (English)In: Stat, E-ISSN 2049-1573, Vol. 8, no 1, article id e240Article in journal (Refereed) Published
Abstract [en]

We propose a new nonparametric clustering method for dependent functional data, the double clustering bagging Voronoi method. It consists of two levels of clustering. Given a spatial lattice of points, a function is observed at each grid point. In the first‐level clustering, features of the functional data are clustered. The second‐level clustering takes dependence into account, by grouping local representatives, built from the resulting first‐level clusters, using a bagging Voronoi strategy. Depending on the distance measure used, features of the functions may be included in the second‐step clustering, making the method flexible and general. Combined with the clustering method, a multiresolution approach is proposed that searches for stable clusters at different spatial scales, aiming to capture latent structures. This provides a powerful and computationally efficient tool to cluster dependent functional data at different spatial scales, here illustrated by a simulation study. The introduced methodology is applied to varved lake sediment data, aiming to reconstruct winter climate regimes in northern Sweden at different time resolutions over the past 6,000 years.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
bagging Voronoi strategy, climate reconstruction, clustering, dependency, functional data
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-164004 (URN)10.1002/sta4.240 (DOI)000506857900010 ()2-s2.0-85081025918 (Scopus ID)
Funder
Swedish Research Council, 340-2013-5203Swedish Research Council, 2016-02763
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2023-03-24Bibliographically approved
Rani, R., Abramowicz, K., Falster, D. S., Sterck, F. & Brännström, Å. (2018). Effects of bud-flushing strategies on tree growth. Tree Physiology, 38(9), 1384-1393
Open this publication in new window or tab >>Effects of bud-flushing strategies on tree growth
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2018 (English)In: Tree Physiology, ISSN 0829-318X, E-ISSN 1758-4469, Vol. 38, no 9, p. 1384-1393Article in journal (Refereed) Published
Abstract [en]

Allocation of carbohydrates between competing organs is fundamental to plant development, growth and productivity. Carbohydrates are synthesized in mature leaves and distributed via the phloem vasculature to developing buds where they are consumed to produce new biomass. The distribution and mass-allocation processes within the plant remain poorly understood and may involve complex feedbacks between different plant functions, with implications for the emergent structure of the plant. Here, we investigate how the order in which dormant buds are flushed affects the development of tree size and reproductive output during the first 20 years of growth in full light and shaded canopy environments. We report the following findings: (i) Bud-flushing strategies strongly affect the temporal dynamics of height, mass and the size of reproduction pool, as well as the resulting architectures. (ii) Bud-flushing strategies affect tree growth by altering the rate of growth and final size of trees. (iii) No single bud-flushing strategy performs best when both the size and allocation for reproduction of the resulting trees are compared. However, we observe that the strategy that optimizes the net carbon gain for the entire tree architecture always results in a high reproduction output. (iv) Branch turnover and meristem regeneration enhance the performance of certain strategies with respect to the measured quantities. These results highlight the importance of employing generic models of architecture (i.e., non-species-specific) to identify general mechanisms of carbon allocation and the spatial distribution of newly formed biomass in growing trees.

Place, publisher, year, edition, pages
Oxford University Press, 2018
Keywords
bud flushing, carbon allocation, functional structural plant model, tree architecture
National Category
Forest Science
Identifiers
urn:nbn:se:umu:diva-155037 (URN)10.1093/treephys/tpy005 (DOI)000452456200011 ()29534227 (PubMedID)2-s2.0-85054756071 (Scopus ID)
Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2023-03-24Bibliographically approved
Wenk, E. H., Abramowicz, K., Westoby, M. & Falster, D. S. (2018). Investment in reproduction for 14 iteroparous perennials is large and associated with other life-history and functional traits. Journal of Ecology, 106(4), 1338-1348
Open this publication in new window or tab >>Investment in reproduction for 14 iteroparous perennials is large and associated with other life-history and functional traits
2018 (English)In: Journal of Ecology, ISSN 0022-0477, E-ISSN 1365-2745, Vol. 106, no 4, p. 1338-1348Article in journal (Refereed) Published
Abstract [en]

1. While theoretical models predict reproductive allocation (RA) should approach 100% of available energy as a plant ages, available empirical data suggest much lower RA values in perennial plants. In this study, we have two aims. First, we assess whether the discrepancy between theory and data arises from methodological differences in how growth and RA are calculated. Specifically, we hypothesize RA in older plants is large when compared to growth in leaf area, that is, after excluding turnover of stem and leaf tissues. Second, we hypothesize that species with cheap tissues or those that are shorter reach RA = 0.5 at a younger age.

2. We measured investment in leaf, stem and reproduction on individuals from 14 co-occurring woody perennial iteroparous species. A fire chronosequence allowed us to use a space-for-time substitution to estimate RA schedules for each species, simultaneously measuring reproductive and vegetative production on individuals differing in age.

3. For most (11 of 14) species, we found RA eventually reached 100% of available energy, with another two species reaching at least 80%. Increases in RA were associated with a decline in growth of leaf area. Comparing species, we found that species with cheap leaves reached RA = 0.5 sooner (they could be called fast-living), whereas delayed maturation and slower increases in RA were associated with greater maximum height.

4. Synthesis. Explicitly accounting for the cost of leaf replacement leads to the high estimates of reproductive allocation in perennial plants predicted by theoretical models, limiting or even halting leaf area expansion. For some species, so much energy is allocated to reproduction that leaf area declines year-upon-year for multiple growing seasons preceding death. Connecting lifetime reproductive allocation schedules with leaf area expansion, leaf life span, and plant maximum height demonstrates how reproductive allocation schedules synthesize a plant's life-history strategy, making them a valuable tool for connecting plant traits and demography.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
big-bang, ecological strategies, life history, plant species traits, population dynamics, reproduction, reproductive allocation
National Category
Environmental Sciences Ecology
Identifiers
urn:nbn:se:umu:diva-150762 (URN)10.1111/1365-2745.12974 (DOI)000435444700002 ()2-s2.0-85048615267 (Scopus ID)
Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2023-03-24Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9040-6674

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