Working papers

Vihola M, Helske J, Franks, J (2018). Importance sampling type estimators based on approximate marginal MCMC. On ArXiv.

Voutilainen M, Helske J, Högmander, H (2018). A Bayesian modeling of historical demographic development: Finland 1648-1850.

Peer-reviewed papers and book chapters

Lindsten, F, Helske J, Vihola M (2018). Graphical model inference: Sequential Monte Carlo meets deterministic approximations. NIPS2018.

Helske S, Helske J, Eerola M (2018). Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data. In: Ritschard G, Studer M (eds) Sequence Analysis and Related Approaches. Life Course Research and Social Policies, vol 10. Springer, Cham.

Helske S, Helske J (2018). Mixture hidden Markov models for sequence data: the seqHMM package in R. Accepted to Journal of Statistical Software.

Helske J (2017). KFAS: exponential family state space models in R. Journal of Statistical Software, 78(10):1-39.

Luukko PJJ., Helske J, Räsänen E (2016). Introducing libeemd: a program package for performing the ensemble empirical mode decomposition. Computational Statistics, 31(2):545-557.

Helske J, Nyblom J (2015). Improved frequentist prediction intervals for autoregressive models by simulation. In Siem Jan Koopman and Neil Shephard, editors, Unobserved Components and Time Series Econometrics. Oxford University Press.

Helske J, Nyblom J, Ekholm P, Meissner K (2013). Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models. Environmetrics, 24(4):237–247.

Non-peer-reviewed conference proceedings and book chapters

Helske S, Helske J, Eerola M (2016). Analysing Complex Life Sequence Data with Hidden Markov Modelling. International Conference on Sequence Analysis and Related Methods (LaCOSA II), Lausanne, Switzerland.

Helske J, Nyblom J (2014). Improved frequentist prediction intervals for ARMA models by simulation. In Johan Knif and Bernd Pape, editors, Contributions to Mathematics, Statistics, Econometrics, and Finance: Essays in Honour of Professor Seppo Pynnönen, number 296 in Acta Wasaensia, pages 71–86. University of Vaasa.

Helske J, Eerola M, Tabus I (2010). Minimum description length based hidden Markov model clustering for life sequence analysis. In Proceedings of 2010 Workshop on Information Theoretic Methods in Science and Engineering, Tampere Finland.

Posters

Helske, J, Vihola M, Franks, J (2017). Accelerating MCMC with an approximation - Importance sampling versus delayed acceptance. SMC2017, Uppsala, Sweden.

Helske, J (2017). Bayesian non-Gaussian state space models in R - Random-walk Metropolis versus Hamiltonian Monte Carlo. UseR! 2017, Brussels, Belgium.

Helske, S., Helske, J. (2016). The mixture hidden Markov model for the analysis of Internet usage and well-being among adolescents. International Conference on Sequence Analysis and Related Methods (LaCOSA II), Lausanne, Switzerland.

Helske, J (2016). bssm: Bayesian exponential family state space models in R. Nordstat 2016, Copenhagen, Denmark.

Helske J (2015). KFAS: exponential family state space models in R. UseR! 2015, Aalborg, Denmark. Best poster award.

Helske, S, Helske, J (2015). The seqHMM package: hidden Markov models for life sequences. The useR! 2015, Aalborg, Denmark.

Helske, S, Helske, J, Eerola, M, Tabus, I (2014). Clustering multidimensional life sequences with hidden Markov models. Nordstat 2014, Turku, Finland.

Helske J, Nyblom J, Ekholm P, Meissner K (2013). Estimating yearly nutrient fluxes via Gaussian state space models. Limnological Days 2013, Helsinki, Finland.