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Pya Arnqvist, Natalya, universitetslektor
Alternative names
Publications (10 of 40) 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
Pya Arnqvist, N. (2024). On some extensions of shape-constrained generalized additive modelling in R.
Open this publication in new window or tab >>On some extensions of shape-constrained generalized additive modelling in R
2024 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Regression models that incorporate smooth functions of predictor variables to explain the relationships with a response variable have gained widespread usage and proved successful in various applications. By incorporating smooth functions of predictor variables, these models can capture complex relationships between the response and predictors while still allowing for interpretation of the results. In situations where the relationships between a response variable and predictors are explored, it is not uncommon to assume that these relationships adhere to certain shape constraints. Examples of such constraints include monotonicity and convexity. The scam package for R has become a popular package to carry out the full fitting of exponential family generalized additive modelling with shape restrictions on smooths. The paper aims to extend the existing framework of shape-constrained generalized additive models (SCAM) to accommodate smooth interactions of covariates, linear functionals of shape-constrained smooths and incorporation of residual autocorrelation. The methods described in this paper are implemented in the recent version of the package scam, available on the Comprehensive R Archive Network (CRAN).

Keywords
smoothing, shape constraints, interaction, smooth ANOVA, regression, linear functionals of smooths
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-222486 (URN)10.48550/arXiv.2403.09438 (DOI)
Funder
Swedish Research Council, 2022-04190
Available from: 2024-03-19 Created: 2024-03-19 Last updated: 2024-03-19
Pya Arnqvist, N. (2024). scam: Shape constrained additive models. R package version 1.2-15.
Open this publication in new window or tab >>scam: Shape constrained additive models. R package version 1.2-15
2024 (English)Other (Other academic)
Abstract [en]

scam provides functions for generalized additive modelling under shape constraints on the component functions of the linear predictor of the GAM. Models can contain multiple shape constrained and unconstrained terms as well as bivariate smooths with double or single monotonicity.

Keywords
smoothing, generalized additive model, shape constraints, penalized regression splines
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-220033 (URN)
Available from: 2024-01-26 Created: 2024-01-26 Last updated: 2024-01-26Bibliographically approved
Shcherbak, D. & Pya Arnqvist, N. (2023). Geometry on optimal problem.
Open this publication in new window or tab >>Geometry on optimal problem
2023 (English)Other (Other academic)
Abstract [en]

We introduce an algorithm which can be directly used to feasible and optimum search in linear programming. Starting from an initial point the algorithm iteratively moves a point in a direction to resolve the violated constraints. At the same time, it ensures that previously fulfilled constraints are not breached during this process. The method is based on geometrical properties of n-dimensional space and can be used on any type of linear constraints (>, =, ≥), moreover it can be used when the feasible region is non-full-dimensional.

National Category
Computational Mathematics
Research subject
Mathematics
Identifiers
urn:nbn:se:umu:diva-217575 (URN)10.48550/arXiv.2312.01775 (DOI)
Available from: 2023-12-09 Created: 2023-12-09 Last updated: 2023-12-21
Pya Arnqvist, N., Arnqvist, P. & Sjöstedt de Luna, S. (2022). fdaMocca: Model-Based Clustering for Functional Data with Covariates. R package version 0.1-1.
Open this publication in new window or tab >>fdaMocca: Model-Based Clustering for Functional Data with Covariates. R package version 0.1-1
2022 (English)Other (Other academic)
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-198596 (URN)
Projects
Functional data analysis and spatial statistics
Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2022-08-23Bibliographically 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
Pya Arnqvist, N. (2022). scam: Shape constrained additive models. R package version 1.2-13.
Open this publication in new window or tab >>scam: Shape constrained additive models. R package version 1.2-13
2022 (English)Other (Other academic)
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-199286 (URN)
Available from: 2022-09-12 Created: 2022-09-12 Last updated: 2022-09-12Bibliographically approved
Pya Arnqvist, N., Ngendangenzwa, B., Lindahl, E., Nilsson, L. & Yu, J. (2021). Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers. Econometrics and Statistics, 18, 89-105
Open this publication in new window or tab >>Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers
Show others...
2021 (English)In: Econometrics and Statistics, ISSN 2452-3062, Vol. 18, p. 89-105Article in journal (Refereed) Published
Abstract [en]

One of the primary concerns of product quality control in the automotive industry is an automated detection of defects of small sizes on specular car body surfaces. A new statistical learning approach is presented for surface finish defect detection based on spline smoothing method for feature extraction and k-nearest neighbour probabilistic classifier. Since the surfaces are specular, structured lightning reflection technique is applied for image acquisition. Reduced rank cubic regression splines are used to smooth the pixel values while the effective degrees of freedom of the obtained smooths serve as components of the feature vector. A key advantage of the approach is that it allows reaching near zero misclassification error rate when applying standard learning classifiers. In addition, probability based performance evaluation metrics have been proposed as alternatives to the conventional metrics. The usage of those provides the means for uncertainty estimation of the predictive performance of a classifier. Experimental classification results on the images obtained from the pilot system located at Volvo GTO Cab plant in Umeå, Sweden, show that the proposed approach is much more efficient than the compared methods.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
classification, defect detection, smoothing, EDF, probabilistic k-NN classifier
National Category
Probability Theory and Statistics Signal Processing Manufacturing, Surface and Joining Technology
Research subject
Mathematical Statistics; Automatic Control; Signal Processing
Identifiers
urn:nbn:se:umu:diva-172268 (URN)10.1016/j.ecosta.2020.05.005 (DOI)000636803000008 ()2-s2.0-85087319488 (Scopus ID)
Projects
FIQA
Funder
Vinnova, 2015-03706
Available from: 2020-06-17 Created: 2020-06-17 Last updated: 2022-09-30Bibliographically approved
Pya Arnqvist, N., Arnqvist, P. & Sjöstedt de Luna, S. (2021). fdaMocca: Model-Based Clustering for Functional Data with Covariates. R package version 0.1-0.
Open this publication in new window or tab >>fdaMocca: Model-Based Clustering for Functional Data with Covariates. R package version 0.1-0
2021 (English)Other (Other academic)
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-188794 (URN)
Available from: 2021-10-22 Created: 2021-10-22 Last updated: 2021-10-22Bibliographically approved
Pya Arnqvist, N., Voinod, V., Makarov, R. & Voinov, Y. (2021). nilde: Nonnegative Integer Solutions of Linear Diophantine Equations with Applications. R package version 1.1-4.
Open this publication in new window or tab >>nilde: Nonnegative Integer Solutions of Linear Diophantine Equations with Applications. R package version 1.1-4
2021 (English)Other (Other academic)
National Category
Mathematics
Research subject
Biomedical Radiation Science
Identifiers
urn:nbn:se:umu:diva-180963 (URN)
Available from: 2021-03-04 Created: 2021-03-04 Last updated: 2021-04-16Bibliographically approved
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