Browsing by Subject " statistics "

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  • Shubin, Mikhail (Helsingin yliopisto, 2016)
    The dissertation presents five problem-driven research articles, representing three research domains related to micro-organisms causing infectious disease. Articles I and II are devoted to the A(H1N1)pdm09 influenza (`swine flu') epidemic in Finland 2009-2011. Articles III and IV present software tools for analysing experimental data produced by Biolog phenotype microarrays. Article V studies a mismatch distribution as a summary statistic for the inference about evolutionary dynamics and demographic processes in bacterial populations. All addressed problems share the following two features: (1) they concern a dynamical process developing in time and space; (2) the observations of the process are partial and imprecise. The problems are generally approached using Bayesian Statistics as a formal methodology for learning by confronting hypothesis to evidence. Bayesian Statistics relies on modelling: constructing a generative algorithm mimicking the object, process or phenomenon of interest.
  • Li, Zitong (Helsingin yliopisto, 2014)
    Quantitative trait loci (QTL) /association mapping aims to identify the genomic loci associated with the complex traits. From a statistical perspective, multiple linear regression is often used to model, estimate and test the effects of molecular markers on a trait. With genotype data derived from contemporary genomics techniques, however, the number of markers typically exceed the number of individuals, and it is therefore necessary to perform some sort of variable selection or parameter regularization to provide reliable estimates of model parameters. In addition, many quantitative traits are changing during their development process of life. Accordingly, a longitudinal study that jointly maps the repeated measurements of the phenotype over time may increase the statistical power to identify QTLs, compared with the single trait analysis. In this thesis, a series of Bayesian variable selection/regularization linear methods were developed and applied for analyzing quantitative traits measured at either single or multiple time points. The first work provided an overview of the principal frequentist regularization methods for analyzing single traits. The second work also focused on single trait analysis, where a variational Bayesian (VB) algorithm was derived for estimating parameters in several Bayesian regularization methods. The VB methods can be quickly implemented on large data sets in contrast to the classical Markov Chain Monte Carlo methods. In the third work, the Bayesian regularization method was extended to a non-parametric varying coefficient model to analyze longitudinal traits. Particularly, an efficient VB stepwise algorithm was used for variable selection, so that the method can be quickly implemented even on data sets with a large number of time points and/or a large number of markers. The fourth work is an application of variable selection methods on forest genetics data collected from Northern Sweden. From several conifer wood properties traits with multiple time points, four QTLs located at genes were identified, which are promising targets for future research in wood molecular biology and breeding.
  • Woolley, Skipton; Bax, Nicolas; Currie, Jock; Dunn, Daniel; Hansen, Cecilie; Hill, Nicole; O'Hara, Timothy; Ovaskainen, Otso; Sayre, Roger; Vanhatalo, Jarno; Dunstan, Piers (2020)
    Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.
  • Bhattacharjee, Joy; Marttila, Hannu; Launiainen, Samuli; Lepistö, Ahti; Kløve, Bjørn (Elsevier, 2021)
    Science of The Total Environment 779 (2021), 146419
    Maintaining and improving surface water quality requires knowledge of nutrient and sediment loads due to past and future land-use practices, but historical data on land cover and its changes are often lacking. In this study, we tested whether land-use-specific export coefficients can be used together with satellite images (Landsat) and/or regional land-use statistics to estimate riverine nutrient loads and concentrations of total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). The study area, Simojoki (3160 km2) in northern Finland, has been intensively drained for peatland forestry since the 1960s. We used different approaches at multiple sub-catchment scales to simulate TN, TP, and SS export in the Simojoki catchment. The uncertainty in estimates based on specific export coefficients was quantified based on historical land-use changes (derived from Landsat data), and an uncertainty boundary was established for each land-use. The uncertainty boundary captured at least 60% of measured values of TN, TP, and SS loads or concentrations. However, the uncertainty in estimates compared with measured values ranged from 7% to 20% for TN, 0% to 18% for TP, and 13% to 43% for SS for different catchments. Some discrepancy between predicted and measured loads and concentrations was expected, as the method did not account for inter-annual variability in hydrological conditions or river processes. However, combining historical land-use change estimates with simple export coefficients can be a practical approach for evaluating the influence on water quality of historical land-use changes such as peatland drainage for forest establishment.
  • Lintuvuori, Meri (Helsingfors universitet, 2010)
    The number of Finnish pupils attending special education has increased for more than a decade (Tilastokeskus 1999, 2000, 2001, 2003, 2004, 2005a, 2006b, 2007b, 2008b, 2008e, 2009b; Virtanen ja Ratilainen 1996). In the year 2007 nearly third of Finnish comprehensive school pupils took part in special needs education. According to the latest statistics, in the autumn of 2008 approximately 47 000 pupils have been admitted or transferred to special education and approximately 126 000 pupils received part-time special education during the 2007-2008 academic year. (Tilastokeskus 2008b, 2009b.) The Finnish special education system is currently under review. The Reform, both in legislation and in practice, began nationwide in the year 2008 (e.g. Special education strategy document, November 2007 and the development project Kelpo). The aim of the study was the statistical description of the Finnish special education system and on the other hand to gain a deeper understanding about the Finnish special education system and its quantitative increase, by analysis based on the nationwide statistical information. Earlier studies have shown that the growth in special education is affected by multiple independent variables and cannot be solely explained by the pupil characteristics. The statistical overview and analysis have been carried out in two parts. In the first part, the description and analysis were based on statistical time series from the academic year 1979-1980 until 2008. While, in the second, more detailed description and analysis, based on comparable time series from 1995 to 2008 and from 2001-2002 to 2007-2008, is presented. Historical perspective was one part of this study. There was an attempt to find reasons explaining the observed growth in the special needs education from late 1960s to 2008. The majority of the research was based on the nationwide statistics information. In addition to this, materials including educational legislation literature, different kind of records of special education and preceding studies were also used to support the research. The main results of the study, are two statistical descriptions and time series analysis of the quantitative increase of the special needs education. Further, a summary of the plausible factors behind the special education system change and its quantitative increase, is presented. The conclusions coming from the study can be summarised as follows: the comparable statistical time series analysis suggests that the growth in special education after the year 1999 could be a consequence of the changes in the structure of special education and that new group of pupils have been directed to special needs education.
  • Kela (Kela, 2019)
  • Kela (Kela, 2020)
    Suomen virallinen tilasto
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (2008)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (Kela, Tilastoryhmä, 2009)
  • Kansaneläkelaitos Kela; Folkpensionsanstalten FPA; Social Insurance Institution of Finland Kela (Kela, Tilastoryhmä, 2010)
  • Kansaneläkelaitos Kela; Folkpensionsanstalten FPA; Social Insurance Institution of Finland Kela (Kela, Tilastoryhmä, 2011)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (2012)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (2013)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (2014)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (Kela, 2015)
  • Kansaneläkelaitos Kela; Social Insurance Institution of Finland Kela; Folkpensionsanstalten FPA (Kela, 2016)
  • Kela (Kela, 2017)
  • Kela (Kela, 2018)
  • Kela (Kela, 2019)