| 1. |
- Abramowicz, Konrad, 1983-, et al.
(författare)
-
Domain selection and family-wise error rate for functional data : a unified framework
- 2023
-
Ingår i: Biometrics. - : John Wiley & Sons. - 0006-341X .- 1541-0420. ; 79:2, s. 1119-1132
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 2. |
- Abramowicz, Konrad, 1983-, et al.
(författare)
-
Multiresolution clustering of dependent functional data with application to climate reconstruction
- 2019
-
Ingår i: Stat. - : John Wiley & Sons. - 2049-1573. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 3. |
- Abramowicz, Konrad, 1983-, et al.
(författare)
-
Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data
- 2022
-
Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 177
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 4. |
|
|
| 5. |
- Abramowicz, Konrad, 1983-, et al.
(författare)
-
Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament
- 2018
-
Ingår i: Scandinavian Journal of Statistics. - : John Wiley & Sons. - 0303-6898 .- 1467-9469. ; 45:4, s. 1036-1061
-
Tidskriftsartikel (refereegranskat)abstract
- Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject-specific covariates, we introduce a distribution-free procedure for testing a functional-on-scalar linear model with fixed effects. The procedure does not only test the global hypothesis on the entire domain but also selects the intervals where statistically significant effects are detected. We prove that the proposed tests are provided with an asymptotic control of the intervalwise error rate, that is, the probability of falsely rejecting any interval of true null hypotheses. The procedure is applied to one-leg hop data from a study on anterior cruciate ligament injury. We compare knee kinematics of three groups of individuals (two injured groups with different treatments and one group of healthy controls), taking individual-specific covariates into account.
|
|
| 6. |
- Haemig, Paul D., et al.
(författare)
-
Dynamic table-visiting behavior of birds at outdoor restaurants and cafes
- 2021
-
Ingår i: Ethology. - : John Wiley & Sons. - 0179-1613 .- 1439-0310. ; 127:7, s. 505-516
-
Tidskriftsartikel (refereegranskat)abstract
- Fear of humans and its effect on animal behavior is increasingly being recognized as an important structuring force in ecological landscapes, with consequences for ecological interactions and communities. When aggressive, physically dominant species are displaced by anthropogenic disturbance, physically weaker species exploit competitor and predator downtimes to forage in previously risky places. Birds feeding at outdoor restaurants and cafes in association with humans are exposed to fluctuating levels of perceived danger caused by frequently changing densities of human diners. Consequently, birds must make decisions about which dining tables to visit based on trade-offs between foraging gain and perceived danger from avian competitors and humans. We tested the hypothesis that interspecific differences in response to perceived danger, combined with varying densities of human diners, dynamically alter which bird species predominates at dining tables. We found that house sparrows (Passer domesticus) tolerated higher human diner-densities than larger-sized, more physically dominant Eurasian jackdaws (Coloeus monedula). Sparrows were usually the first birds to visit diner-occupied tables and spent more time there than jackdaws. However, at diner-abandoned tables, this pattern changed: During low diner-densities at surrounding tables, jackdaws were usually the predominant species in first visits and minutes spent visiting, while at high diner-densities sparrows usually predominated. Moreover, along a gradient of increasing human diner-density, sparrows gradually replaced jackdaws as the predominant species in first visits and time at abandoned tables. However, at diner-occupied tables, once a sparrow chose which table to visit, factors other than diner-density influenced its choice of where to forage there (table-top or ground). To our knowledge, our research is the first scientific study of table-visiting behavior by birds at outdoor restaurants and cafes, and the first to reveal interspecific differences in table-visiting behavior by birds there.
|
|
| 7. |
- Haemig, Paul, et al.
(författare)
-
Red fox and tick-borne encephalitis (TBE) in humans: Can predators influence public health?
- 2008
-
Ingår i: Scandinavian Journal of Infectious Diseases. - : Informa UK Limited. - 0036-5548 .- 1651-1980. ; 40:6-7, s. 527-532
-
Tidskriftsartikel (refereegranskat)abstract
- Analysing datasets from hunting statistics and human cases of tick-borne encephalitis (TBE), we found a positive correlation between the number of human TBE cases and the number of red fox (Vulpes vulpes). Time lags were also present, indicating that high numbers of red fox in 1 y translated into high numbers of human TBE cases the following y. Results for smaller predators were mixed and inconsistent. Hares and grouse showed negative correlations with human TBE cases, suggesting that they might function as dilution hosts. Combining our findings with food web dynamics, we hypothesize a diversity of possible interactions between predators and human disease – some predators suppressing a given disease, others enhancing its spread, and still others having no effect at all. Larger-sized predators that suppress red fox numbers and activity (i.e. wolf, Canis lupus; European lynx, Lynx lynx) were once abundant in our study area but have been reduced or extirpated from most parts of it by humans. We ask what would happen to red foxes and TBE rates in humans if these larger predators were restored to their former abundances.Read More: http://informahealthcare.com/doi/abs/10.1080/00365540701805446
|
|
| 8. |
- Kellgren, Therese, 1983-
(författare)
-
Hidden patterns that matter : statistical methods for analysis of DNA and RNA data
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Understanding how the genetic variations can affect characteristics and function of organisms can help researchers and medical doctors to detect genetic alterations that cause disease and reveal genes that causes antibiotic resistance. The opportunities and progress associated with such data come however with challenges related to statistical analysis. It is only by using properly designed and employed tools, that we can extract the information about hidden patterns. In this thesis we present three types of such analysis. First, the genetic variant in the gene COL17A1 that causes corneal dystrophy with recurrent erosions is reveled. By studying Next-generation sequencing data, the order of the nucleotides in the DNAsequence was be obtained, which enabled us to detect interesting variants in the genome. Further, we present results of an experimental design study with the aim to make the best selection from a family that is affected by an inherited disease. In second part of the work, we analyzed a novel antibiotic resistance Staphylococcus epidermidis clone that is only found in northern Europe. By investigating its genetic data, we revealed similarities to a world known antibiotic resistance clone. As a result, the antibiotic resistance profile is established from the DNA sequences. Finally, we also focus on the challenges related to the abundance of genetic data from different sources. The increasing number of public gene expression datasets gives us opportunity to increase our understanding by using information from multiple sources simultaneously. Naturally, this requires merging independent datasets together. However, when doing so, the technical and biological variation in the joined data increases. We present a pre-processing method to construct gene co-expression networks from a large diverse gene-expression dataset.
|
|
| 9. |
- Mörling, Tommy, et al.
(författare)
-
A method to estimate fibre length distribution in conifers based on wood samples from increment cores
- 2003
-
Ingår i: Holzforschung. - 0018-3830. ; 57:3, s. 248-254
-
Tidskriftsartikel (refereegranskat)abstract
- We propose a method to estimate fibre length distribution in conifers based on wood samples from increment cores processed by automatic optical fibre-analysers. Automatic fibre-analysers are unable to distinguish: a) fibres from other tissues, “fines”, and b) cut from uncut fibres. However, our proposed method can handle these problems if the type of distributions that fibre lengths and fines follow is known. In our study the length distributions of fines and fibres were assumed to follow truncated normal distributions, characterised by means and standard deviations of the two distributions. Parameter estimates were obtained by the maximum likelihood method. Wood samples from two 22-year-old Scots pine trees at breast height were used to evaluate the performance of the method. From stem discs at 1.5 m, adjacent samples of 5 mm increment cores and wood pieces were taken. The cores were trimmed 1 mm at each side and samples were, after maceration, analysed in a Kajaani FiberLab 3.0. The results showed that the method works well and gives a possibility to distinguish fine and fibre length distribution.
|
|
| 10. |
- Pataky, Todd Colin, et al.
(författare)
-
Simultaneous inference for functional data in sports biomechanics : Comparing statistical parametric mapping with interval-wise testing
- 2023
-
Ingår i: AStA Advances in Statistical Analysis. - : Springer. - 1863-8171 .- 1863-818X. ; 107, s. 369-392
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 11. |
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
- Schelin, Lina, 1980-, et al.
(författare)
-
Construction of kriging prediction intervals for non-Gaussian spatial processes
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this article, we compare three methods to construct prediction intervals for the value of a stationary process, based on plug-in ordinary kriging predictors. Ordinary kriging is a widely used method for prediction that, given observations of a (spatial) process, forms the best linear unbiased predictor of the process at a new location. Construction of prediction intervals for the value of interest based on ordinary kriging predictors typically rely on Gaussian assumptions. Special attention is here given to non-Gaussian processes, where construction of such intervals is less straightforward. Methods based on asymptotic normality, Gaussian transformations and semiparametric bootstrap are compared on simulated and real data. The study suggests that the semiparametric method (that does not rely on distributional assumptions) is robust and is to be recommended for non-Gaussian processes. For practitioners the semiparametric method is an attractive alternative since the method can be used without spcifying a link function or making distributional assumptions.
|
|
| 17. |
- Schelin, Lina, 1980-, et al.
(författare)
-
Kriging prediction intervals based on semiparametric bootstrap
- 2010
-
Ingår i: Mathematical Geosciences. - : Springer Verlag. - 1874-8961 .- 1874-8953. ; 42:8, s. 985-1000
-
Tidskriftsartikel (refereegranskat)abstract
- Kriging is a widely used method for prediction, which, given observations of a (spatial) process, yields the best linear unbiased predictor of the process at a new location. The construction of corresponding prediction intervals typically relies on Gaussian assumptions. Here we show that the distribution of kriging predictors for non-Gaussian processes may be far from Gaussian, even asymptotically. This emphasizes the need for other ways to construct prediction intervals. We propose a semiparametric bootstrap method with focus on the ordinary kriging predictor. No distributional assumptions about the data generating process are needed. A simulation study for Gaussian as well as lognormal processes shows that the semiparametric bootstrap method works well. For the lognormal process we see significant improvement in coverage probability compared to traditional methods relying on Gaussian assumptions.
|
|
| 18. |
- Schelin, Lina, 1980-, et al.
(författare)
-
Spatial prediction in the presence of left-censoring
- 2014
-
Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 74, s. 125-141
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Environmental (spatial) monitoring of different variables often involves left-censored observations falling below the minimum detection limit (MDL) of the instruments used to quantify them. Several methods to predict the variables at new locations given left-censored observations of a stationary spatial process are compared. The methods use versions of kriging predictors, being the best linear unbiased predictors minimizing the mean squared prediction errors. A semi-naive method that determines imputed values at censored locations in an iterative algorithm together with variogram estimation is proposed. It is compared with a computationally intensive method relying on Gaussian assumptions, as well as with two distribution-free methods that impute the MDL or MDL divided by two at the locations with censored values. Their predictive performance is compared in a simulation study for both Gaussian and non-Gaussian processes and discussed in relation to the complexity of the methods from a user’s perspective. The method relying on Gaussian assumptions performs, as expected, best not only for Gaussian processes, but also for other processes with symmetric marginal distributions. Some of the (semi-)naive methods also work well for these cases. For processes with skewed marginal distributions (semi-)naive methods work better. The main differences in predictive performance arise for small true values. For large true values no difference between methods is apparent.
|
|
| 19. |
- Sedaghat, Mina, et al.
(författare)
-
DieHard: Reliable Scheduling to Survive Correlated failures in Cloud Data Centers
- 2016
-
Ingår i: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). - : IEEE.
-
Konferensbidrag (refereegranskat)abstract
- In large scale data centers, a single fault can lead to correlated failures of several physical machines and the tasks running on them, simultaneously. Such correlated failures can severely damage the reliability of a service or a job running on the failed hardware. This paper models the impact of stochastic and correlated failures on job reliability in a data center. We focus on correlated failures caused by power outages or failures of network components, on jobs running multiple replicas of identical tasks. We present a statistical reliability model and an approximation technique for computing a job’s reliability in the presence of correlated failures. In addition, we address the problem of scheduling a job with reliability constraints.We formulate the scheduling problem as an optimization problem, with the aim being to maintain the desired reliability with the minimum number of extra tasks to resist failures.We present a scheduling algorithm that approximates the minimum number of required tasks and a placement to achieve a desired job reliability. We study the efficiency of our algorithm using an analytical approach and by simulating a cluster with different failure sources and reliabilities. The results show that the algorithm can effectively approximate the minimum number of extra tasks required to achieve the job’s reliability.
|
|
| 20. |
- Sjöstedt de Luna, Sara, 1964-, et al.
(författare)
-
Non-destructive methods for assessing tree fiber length distributions in standing trees
- 2021
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 21. |
|
|
| 22. |
- Strandberg, Johan, et al.
(författare)
-
A comparison of spatiotemporal and functional kriging approaches
- 2021
-
Ingår i: Geostatistical functional data analysis. - : John Wiley & Sons. - 9781119387916 - 9781119387848 ; , s. 375-402
-
Bokkapitel (refereegranskat)abstract
- Here we present and compare functional and spatiotemporal (Sp.T.) kriging approaches to predict spatial functional random processes, which can also be viewed as Sp.T. random processes. Comparisons are focused on Sp.T. kriging versus ordinary kriging for functional data (OKFD), since more flexible functional kriging approaches like pointwise functional kriging and functional kriging total model coincide with OKFD in several situations. Prediction performance is evaluated via functional cross-validation on simulated data as well as on a Canadian weather data set. The two kriging approaches perform in many cases rather equal for stationary Sp.T. processes. For nonstationary Sp.T. processes, OKFD performs better than Sp.T. kriging. The computational time for OKFD is considerably lower compared to those for the Sp.T. kriging methods.
|
|
| 23. |
- Strandberg, Johan, 1981-
(författare)
-
Non-parametric methods for functional data
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis we develop and study non-parametric methods within three major areas of functional data analysis: testing, clustering and prediction. The thesis consists of an introduction to the field, a presentation and discussion of the three areas, and six papers.In Paper I, we develop a procedure for testing for group differences in functional data. In case of significant group differences, the test procedure identifies which of the groups that significantly differ, and also the parts of the domain they do so, while controlling the type I error of falsely rejecting the null hypothesis. In Paper II, the methodology introduced in Paper I is applied to knee kinematic curves from a one-leg hop for distance to test for differences within and between three groups of individuals (with and without knee deficits). It was found that two of the groups differed in their knee kinematics. We also found that the individual kinematic patterns differed between the two legs in one of the groups. In Paper III, we test for group differences in three groups with respect to joint kinematics from a vertical one-leg hop using a novel method that allows accounting for multiple joints at the same time. The aim of Paper III, as one of few within the field of biomechanics, is to illustrate how different choices prior to the analysis can result in different contrasting conclusions. Specifically, we show how the conclusions depend on the choice of type of movement curve, the choice of leg for between-group comparisons and the included joints.In Paper IV, we present a new non-parametric clustering method for dependent functional data, the double clustering bagging Voronoi method. The objective of the method is to identify latent group structures that slowly vary over domain and give rise to different frequency patterns of functional data object types. The method uses a bagging strategy based on random Voronoi tessellations in which local representatives are formed and clustered. Combined with the clustering method, we also propose a multiresolution approach which allows identification of latent structures at different scales. A simulated dataset is used to illustrate the method's potential in finding stable clusters at different scales. The method is also applied to varved lake sediment data with the aim of reconstructing the climate over the past 6000 years, at different resolutions. In Paper V, we expand and modify the bagging strategy used in Paper IV, by considering different methods of generating the tessellations and clustering the local representatives of the tessellations. We propose new methods for clustering dependent categorical data (e.g., labelled functional data) along a one-dimensional domain, which we also compare in a simulation study. In Paper VI, two kriging approaches to predict spatial functional processes are compared, namely functional kriging and spatio-temporal kriging. A simulation study is conducted to compare their prediction performance and computational times. The overall results show that prediction performance is about the same for stationary spatio-temporal processes while functional kriging works better for non-stationary spatio-temporal processes. Furthermore, the computational time for (ordinary) kriging for functional data, was considerably lower than spatio-temporal kriging. Conditions are also formulated under which it is proved that the two functional kriging methods: ordinary kriging for functional data and pointwise functional kriging coincide.
|
|
| 24. |
- Strandberg, Johan, et al.
(författare)
-
Prediction of spatial functional random processes : comparing functional and spatio-temporal kriging approaches
- 2019
-
Ingår i: Stochastic environmental research and risk assessment (Print). - : Springer. - 1436-3240 .- 1436-3259. ; 33:10, s. 1699-1719
-
Tidskriftsartikel (refereegranskat)abstract
- We present and compare functional and spatio-temporal (Sp.T.) kriging approaches to predict spatial functional random processes (which can also be viewed as Sp.T. random processes). Comparisons with respect to computational time and prediction performance via functional cross-validation is evaluated, mainly through a simulation study but also on a real data set. We restrict comparisons to Sp.T. kriging versus ordinary kriging for functional data (OKFD), since the more flexible functional kriging approaches pointwise functional kriging (PWFK) and the functional kriging total model coincide with OKFD in several situations. Here we formulate conditions under which we show that OKFD and PWFK coincide. From the simulation study, it is concluded that the prediction performance of the two kriging approaches in general is rather equal for stationary Sp.T. processes. However, functional kriging tends to perform better for small sample sizes, while Sp.T. kriging works better for large sizes. For non-stationary Sp.T. processes, with a common deterministic time trend and/or time varying variances and dependence structure, OKFD performs better than Sp.T. kriging irrespective of the sample size. For all simulated cases, the computational time for OKFD was considerably lower compared to those for the Sp.T. kriging methods.
|
|
| 25. |
- Svensson, Ingrid, 1961-, et al.
(författare)
-
Asymptotic properties of a stochastic EM algorithm for mixtures with censored data
- 2010
-
Ingår i: Journal of Statistical Planning and Inference. - : Elsevier. - 0378-3758 .- 1873-1171. ; 140:1, s. 111-127
-
Tidskriftsartikel (refereegranskat)abstract
- Weak consistency and asymptotic normality is shown for a stochastic EM algorithm for censored data from a mixture of distributions under lognormal assumptions. The asymptotic properties hold for all parameters of the distributions, including the mixing parameter. In order to make parameter estimation meaningful it is necessary to know that the censored mixture distribution is identifiable. General conditions under which this is the case are given. The stochastic EM algorithm addressed in this paper is used for estimation of wood fibre length distributions based on optically measured data from cylindric wood samples (increment cores).
|
|
| 26. |
|
|
