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Staff photo Natalya Pya Arnqvist

Natalya Pya Arnqvist

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

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Affiliation
Location
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:

 

Advances in signal processing: reviews. Volume 2, International Frequency Sensor Association Publishing 2021 : 309-342
Pya Arnqvist, Natalya; Lindahl, Eric; Yu, Jun
Econometrics and Statistics, Elsevier 2021, Vol. 18 : 89-105
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.
Winter Conference in Statistics 2019 - Machine Learning, March 10-14, 2019, Hemavan, Sweden
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Lindahl, Eric; et al.
CRoNoS & MDA 2019
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Pya Arnqvist, Natalya; Sjöstedt de Luna, Sara; Abramowicz, Konrad
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
SweDS2018, Umeå University, Sweden, November 20-21, 2018
Pya Arnqvist, Natalya; Ngendangenzwa, Blaise; Nilsson, Leif; et al.
Mathematical Journal, Vol. 18, (2) : 47-58
Voinov, Vassilly; Pya Arnqvist, Natalya; Voinov, Yevgeniy
Mathematical Journal, Vol. 17, (1) : 69-76
Voinov, Vassilly; Pya Arnqvist, Natalya
Statistical Science, Institute of Mathematical Statistics 2016, Vol. 31, (1) : 96-118
Fasiolo, Matteo; Pya, Natalya; Wood, Simon N.
Pya Arnqvist, Natalya; Voinov, Vassilly; Makarov, Rashid; et al.
Stochastic and data analysis methods and applications in statistics and demography: book 2, ISAST 2016 : 667-686
Pya, Natalya; Kussainov, Arman
Forest Ecosystems, Springer 2016, Vol. 3
Pya, Natalya; Schmidt, Matthias
Communications in Statistics - Theory and Methods, Taylor & Francis 2016, Vol. 45, (11) : 3249-3263
Voinov, Vassilly; Pya Arnqvist, Natalya; Makarov, Rashid; et al.
Journal of the American Statistical Association, Vol. 111, (516) : 1548-1563
Wood, Simon N.; Pya Arnqvist, Natalya; Safken, Benjamin
Statistics and computing, Springer 2015, Vol. 25, (3) : 543-559
Pya, Natalya; Wood, Simon N.
Vestnik KazNU/ Physics, Vol. 1 : 98-101
Kussainov, Arman; Karimova, A.; Kussainov, S.; et al.
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.
Communications in statistics. Simulation and computation, Taylor & Francis 2013, Vol. 42, (5) : 1003-1012
Voinov, Vassilly; Pya, Natalya; Shapakov, Niyaz; et al.
AFBE Journal, Vol. 5, (2) : 201-218
Voinov, Vassilly; Pya, Natalya; Makarov, Rashid; et al.
Vestnik KazNU/ Physics, Vol. 3, (38) : 53-58
Kussainov, Arman; Pya, Natalya
Communications in Statistics - Theory and Methods, Taylor & Francis 2010, Vol. 39, (3) : 452-459
Voinov, Vassilly; Pya, Natalya
Communications in statistics. Simulation and computation, Taylor & Francis 2009, Vol. 38, (2) : 355-367
Voinov, Vassilly; Pya Arnqvist, Natalya; Alloyarova, Roza
Statistical models and methods for biomedical and technical systems, Birkhäuser Verlag 2008 : 241-258
Voinov, Vassilly; Alloyarova, Roza; Pya Arnqvist, Natalya
Mathematical methods in survival analysis, reliability and quality of life, John Wiley & Sons 2008 : 189-202
Voinov, Vassilly; Alloyarova, Roza; Pya, Natalya
Communications in Dependability and Quality Management, Vol. 10, (1) : 5-15
Alloyarova, Roza; Nikulin, Mikhail; Pya, Natalya; et al.
Recent advances in stochastic modelling and data analysis, World Scientific 2007 : 243-250
Voinov, Vassilly; Nikulin, Mikhail; Pya, Natalya

Research projects

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