| 1. |
- Helske, Jouni, et al.
(författare)
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Can Visualization Alleviate Dichotomous Thinking? : Effects of Visual Representations on the Cliff Effect
- 2021
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE COMPUTER SOC. - 1077-2626 .- 1941-0506. ; 27:8, s. 3397-3409
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Tidskriftsartikel (refereegranskat)abstract
- Common reporting styles for statistical results in scientific articles, such as p-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers subjective confidence in the results. We also asked the respondents opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the cliff effect: the sudden drop in confidence around p-value 0.05 - compared with classic CI visualization and textual representation of the CI with p-values. All data and analyses are publicly available at https://github.com/helske/statvis.
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| 2. |
- Helske, Satu, et al.
(författare)
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Analysing Complex Life Sequence Data with Hidden Markov Modelling
- 2016
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Ingår i: Proceedings of the International Con-ference on Sequence Analysis and Related Methods, Lausanne, June 8-10,2016, pp 209-240. - : LaCOSA II.
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Konferensbidrag (refereegranskat)abstract
- When analysing complex sequence data with multiple channels (dimen- sions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses.
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| 3. |
- Helske, Satu, 1985-, et al.
(författare)
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Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
- 2018
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Ingår i: Sequence Analysis and Related Approaches. - Switzerland : Springer. - 9783319954202 - 9783319954196 ; , s. 185-200
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Bokkapitel (refereegranskat)abstract
- Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.
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| 4. |
- Helske, Satu, et al.
(författare)
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From Sequences to Variables: Rethinking the Relationship between Sequences and Outcomes
- 2024
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Ingår i: Sociological methodology. - : SAGE PUBLICATIONS INC. - 0081-1750 .- 1467-9531. ; 54:1, s. 27-51
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Tidskriftsartikel (refereegranskat)abstract
- Sequence analysis is increasingly used in the social sciences for the holistic analysis of life-course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a regression model. This approach may be problematic, as cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analyses. Furthermore, it is often more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for uncertain and mixed memberships may lead to wrong conclusions about the nature of the studied relationships. In this article, the authors bring forward and discuss the problems of the "traditional" use of sequence analysis clusters as variables and compare four approaches for creating explanatory variables from sequence dissimilarities using different types of data. The authors conduct simulation and empirical studies, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust analyses accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions, and similarity-based approaches such as representativeness should be preferred.
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| 5. |
- Helske, Satu, 1985-, et al.
(författare)
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
- 2019
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Ingår i: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:3, s. 1-32
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Tidskriftsartikel (refereegranskat)abstract
- Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.less thanbr /greater thanComment: 33 pages, 8 figures
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| 6. |
- Helske, Jouni, et al.
(författare)
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Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models
- 2013
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Ingår i: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 24:4, s. 237-247
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Tidskriftsartikel (refereegranskat)abstract
- Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to estimate aggregated yearly phosphorus and nitrogen fluxes, and their confidence intervals.The effect of model uncertainty is evaluated through a Monte Carlo experiment, where randomly selected sets of nutrient measurements are removed and then predicted by the remaining values together with re-estimated parameters. Results show that our model performs well for rivers with long-term records of flow. Finally, despite the drastic decreases in nutrient loads on the agricultural catchments of the rivers over the last 25years, we observe no corresponding trends in riverine nutrient fluxes.
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| 7. |
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| 8. |
- Helske, Jouni, et al.
(författare)
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Improved frequentist prediction intervals for autoregressive models by simulation
- 2015
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Ingår i: Unobserved Components and Time Series Econometrics. - Oxford : Oxford University Press. - 9780199683666 - 9780191763298 ; , s. 291-309
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- It is well known that the so-called plug-in prediction intervals for autoregressive processes, with Gaussian disturbances, are too short, i.e. the coverage probabilities fall below the nominal ones. However, simulation experiments show that the formulas borrowed from the ordinary linear regression theory yield one-step prediction intervals, which have coverage probabilities very close to that claimed. From a Bayesian point of view the resulting intervals are posterior predictive intervals when uniform priors are assumed for both autoregressive coefficients and logarithm of the disturbance variance. This finding enables one to see how to treat multi-step prediction intervals that are obtained by simulation either directly from the posterior distribution or using importance sampling. An application of the method to forecasting the annual gross domestic product growth in the United Kingdom and Spain is given for the period 2002 to 2011 using the estimation period 1962 to 2001.
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| 9. |
- Helske, Jouni
(författare)
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KFAS : Exponential Family State Space Models in R
- 2017
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 78:10
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Tidskriftsartikel (refereegranskat)abstract
- State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modeling is presented.
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| 10. |
- Helske, Jouni, et al.
(författare)
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Minimum description length based hidden Markov model clustering for life sequence analysis
- 2010
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Ingår i: Proceedings of the Third Workshop on Information Theoretic Methods in Science and Engineering, August 16-18, 2010, Tampere, Finland.
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Konferensbidrag (refereegranskat)abstract
- In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures.
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