Associate Professor in Mathematical Statistics. Main research interests: statistical regression modelling and functional data analysis.

Associate professor at
Department of Mathematics and Mathematical Statistics

MIT-huset, plan 3, Matematik och matematisk statistik, MIT.F.340
Umeå universitet,
901 87
Umeå

**Short track:**

2019 - present: Associate Professor, Umeå University

2017 - 2019: Project assistant, Umeå University

2016 - 2017: Senior research engineer, Umeå University

2015 - 2020: Assistant Professor (on leave 2016-2020), Nazarbayev University, Kz

2011 - 2015: Research Associate, University of Bath, UK

2010 - 2011: Assistant Professor, KIMEP University, Kz

2006 - 2017: Senior research fellow (part-time), Institute for Mathematics, Kz

2007 - 2010: PhD in Statistics, University of Bath, UK

2005 - 2007: Senior Lecturer, KIMEP University, Kz

2000 - 2005: CSc, Candidate of Physical and Math Sciences, Institute for Mathematics, Kz

**Research:**

I have two main research interests, statistical regression modelling and functional data analysis. I am specifically interested in developing methods for shape preserving smoothing within generalized additive models and applications of shape constrained additive models (SCAMs). I've written an R package `scam' which implements SCAM. The short description of this package is given below. I am also interested in methods for clustering functional data with optional scalar covariates and applications of functional clustering.

**scam:** Shape constrained additive models

- `scam' is an R package that implements generalized additive modelling under shape constraints on the component functions of the linear predictor.
- Models can contain multiple shape constrained and unconstrained terms as well as bivariate smooths with double or single monotonicity.
- Univariate smooths under various possible shape constraints including monotonically increasing/decreasing, convex/concave, increasing/decreasing and convex, increasing/decreasing and concave, are available as model terms.
- `scam' implements tensor product smooths for creating bivariate functions with shape constraints in one of the covariates or both covariates.
- The model set up is the same as in `gam' in the package `mgcv' with the added shape constrained smooths. So the unconstrained smooths can be of more than one variable. Other user defined smooths can be also included as model terms.
- `scam' is based on penalized regression splines with automatic smoothness estimation.
- Smoothness selection in `scam' is by GCV or UBRE/AIC.
- A Bayesian approach is used to obtain a covariance matrix of the model coefficients and credible intervals for each smooth.
- as in `gam' in the package `mgcv' the linear preditor of a model in `scam' can depend on a bounded linear functional of a smooth (via a summation convention used in model specification). This allows scalar-on-function regression to be performed.

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**nilde: **Nonnegative integer solutions of linear diophantine equations with applications. N. Pya Arnqvist, V. Voinov, Y. Voinov

- 'nilde' is an R package that provides functions for enumerating all existing nonnegative integer solutions of a linear Diophantine equation.
- 'nilde' also includes functions for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; a problem of additive partitioning of natural numbers; and one-dimensional bin-packing problem
- The algorithm is based on a generating function of Hardy and Littlewood used by Voinov and Nikulin (1997)

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Some other R packages:

**fiberLD:**Fiber Length Determination. N. Pya Arnqvist, S. Sjöstedt de Luna, K. Abramowicz**mvnTest:**Goodness of Fit Tests for Multivariate Normality. N. Pya, V. Voinov, R. Makarov, Y. Voinov

2021

Advances in signal processing: reviews. Volume 2, International Frequency Sensor Association Publishing 2021 : 309-342

Pya Arnqvist, Natalya; Lindahl, Eric; Yu, Jun

2021

Econometrics and Statistics, Elsevier 2021, Vol. 18 : 89-105

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.

2021

Pya Arnqvist, Natalya; Voinod, Vassilly; Makarov, Rashid; et al.

2019

A statistical learning approach for defect detection and classification on specular carbody surfaces

Winter Conference in Statistics 2019 - Machine Learning, March 10-14, 2019, Hemavan, Sweden

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.

2019

CRoNoS & MDA 2019

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.

2019

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.

2019

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.

2019

Pya Arnqvist, Natalya; Sjöstedt de Luna, Sara; Abramowicz, Konrad

2019

Pya Arnqvist, Natalya; Voinov, Vassilly; Voinov, Yevgeniy

2018

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.

2018

SweDS2018, Umeå University, Sweden, November 20-21, 2018

Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.

2018

Mathematical Journal, Vol. 18, (2) : 47-58

Voinov, Vassilly; Pya Arnqvist, Natalya; Voinov, Yevgeniy

2017

Mathematical Journal, Vol. 17, (1) : 69-76

Voinov, Vassilly; Pya Arnqvist, Natalya

2016

Statistical Science, Institute of Mathematical Statistics 2016, Vol. 31, (1) : 96-118

Fasiolo, Matteo; Pya, Natalya; Wood, Simon N.

2016

Pya Arnqvist, Natalya; Voinov, Vassilly; Makarov, Rashid; et al.

2016

Pya Arnqvist, Natalya; Wood, Simon

2016

Stochastic and data analysis methods and applications in statistics and demography: book 2, ISAST 2016 : 667-686

Pya, Natalya; Kussainov, Arman

2016

Forest Ecosystems, Springer 2016, Vol. 3

Pya, Natalya; Schmidt, Matthias

2016

Communications in Statistics - Theory and Methods, Taylor & Francis 2016, Vol. 45, (11) : 3249-3263

Voinov, Vassilly; Pya Arnqvist, Natalya; Makarov, Rashid; et al.

2016

Journal of the American Statistical Association, Vol. 111, (516) : 1548-1563

Wood, Simon N.; Pya Arnqvist, Natalya; Safken, Benjamin

2015

Statistics and computing, Springer 2015, Vol. 25, (3) : 543-559

Pya, Natalya; Wood, Simon N.

2013

Vestnik KazNU/ Physics, Vol. 1 : 98-101

Kussainov, Arman; Karimova, A.; Kussainov, S.; et al.

2013

Izvestiya of the National Academy of Sciences of the Republic of Kazakhstan, Physical and Mathematical Series, Vol. 4, (290) : 13-17

Kussainov, Arman; Kussainov, S. G.; Pya, N. Y.

2013

Communications in statistics. Simulation and computation, Taylor & Francis 2013, Vol. 42, (5) : 1003-1012

Voinov, Vassilly; Pya, Natalya; Shapakov, Niyaz; et al.

2012

AFBE Journal, Vol. 5, (2) : 201-218

Voinov, Vassilly; Pya, Natalya; Makarov, Rashid; et al.

2011

Vestnik KazNU/ Physics, Vol. 3, (38) : 53-58

Kussainov, Arman; Pya, Natalya

2010

Communications in Statistics - Theory and Methods, Taylor & Francis 2010, Vol. 39, (3) : 452-459

Voinov, Vassilly; Pya, Natalya

2009

Communications in statistics. Simulation and computation, Taylor & Francis 2009, Vol. 38, (2) : 355-367

Voinov, Vassilly; Pya Arnqvist, Natalya; Alloyarova, Roza

2008

Statistical models and methods for biomedical and technical systems, Birkhäuser Verlag 2008 : 241-258

Voinov, Vassilly; Alloyarova, Roza; Pya Arnqvist, Natalya

2008

Mathematical methods in survival analysis, reliability and quality of life, John Wiley & Sons 2008 : 189-202

Voinov, Vassilly; Alloyarova, Roza; Pya, Natalya

2007

Communications in Dependability and Quality Management, Vol. 10, (1) : 5-15

Alloyarova, Roza; Nikulin, Mikhail; Pya, Natalya; et al.

2007

Recent advances in stochastic modelling and data analysis, World Scientific 2007 : 243-250

Voinov, Vassilly; Nikulin, Mikhail; Pya, Natalya

1 October 2021 until 30 September 2023

1 January 2016 until 30 June 2019

I have been teaching undergraduate, graduate and executive MBA courses in the areas of probability, statistics, applied statistical and quantitative methods, business time series forecasting, regression analysis, and design of experiments.

Currently, I am involved in teaching the following courses:

- Design of experiments and advanced statistical modelling
- Statistics for engineers
- Analysis of field data