Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Toxicant distributions and impact models in environmental risk analysis of waste sites

Myrkyllisten aineiden jakaumat ja vaikutusmallit jätealueiden ympäristöriskien analyysissä.

Finite-State Spell-Checking with Weighted Language and Error Models

In this paper we present simple methods for construction and evaluation of finite-state spell-checking tools using an existing finite-state lexical automaton, freely available finite-state tools and Internet corpora acquired from projects such as Wikipedia. As an example, we use a freely available open-source implementation of Finnish morphology, made with traditional finite-state morphology tools, and demonstrate rapid building of Northern Sámi and English spell checkers from tools and resources available from the Internet.

An Empirical Comparison of Linear and Nonlinear Volatility Models for Nordic Stock Returns

This paper examines how volatility in financial markets can preferable be modeled. The examination investigates how good the models for the volatility, both linear and nonlinear, are in absorbing skewness and kurtosis. The examination is done on the Nordic stock markets, including Finland, Sweden, Norway and Denmark. Different linear and nonlinear models are applied, and the results indicates that a linear model can almost always be used for modeling the series under investigation, even though nonlinear models performs slightly better in some cases. These results indicate that the markets under study are exposed to asymmetric patterns only to a certain degree. Negative shocks generally have a more prominent effect on the markets, but these effects are not really strong. However, in terms of absorbing skewness and kurtosis, nonlinear models outperform linear ones.

Boundaries in Action : An Activity-theoretical Study of Development, Learning, and Change in Health Care Organization for Patients with Multiple and Chronic Illnesses

This study examines boundaries in health care organizations. Boundaries are sometimes considered things to be avoided in everyday living. This study suggests that boundaries can be important temporally and spatially emerging locations of development, learning, and change in inter-organizational activity. Boundaries can act as mediators of cultural and social formations and practices. The data of the study was gathered in an intervention project during the years 2000-2002 in Helsinki in which the care of 26 patients with multiple and chronic illnesses was improved. The project used the Change Laboratory method that represents a research assisted method for developing work. The research questions of the study are: (1) What are the boundary dynamics of development, learning, and change in health care for patients with multiple and chronic illnesses? (2) How do individual patients experience boundaries in their health care? (3) How are the boundaries of health care constructed and reconstructed in social interaction? (4) What are the dynamics of boundary crossing in the experimentation with the new tools and new practice? The methodology of the study, the ethnography of the multi-organizational field of activity, draws on cultural-historical activity theory and anthropological methods. The ethnographic fieldwork involves multiple research techniques and a collaborative strategy for raising research data. The data of this study consists of observations, interviews, transcribed intervention sessions, and patients' health documents. According to the findings, the care of patients with multiple and chronic illnesses emerges as fragmented by divisions of a patient and professionals, specialties of medicine and levels of health care organization. These boundaries have a historical origin in the Finnish health care system. As an implication of these boundaries, patients frequently experience uncertainty and neglect in their care. However, the boundaries of a single patient were transformed in the Change Laboratory discussions among patients, professionals and researchers. In these discussions, the questioning of the prevailing boundaries was triggered by the observation of gaps in inter-organizational care. Transformation of the prevailing boundaries was achieved in implementation of the collaborative care agreement tool and the practice of negotiated care. However, the new tool and practice did not expand into general use during the project. The study identifies two complementary models for the development of health care organization in Finland. The 'care package model', which is based on productivity and process models adopted from engineering and the 'model of negotiated care', which is based on co-configuration and the public good.

Early-onset depressive disorders,related mental health disorders and substance use-A prospective : longitudinal study of Finnish twins born 1983-1987

Early-onset psychiatric illnesses effects scatter to academic achievements as well as functioning in familial and social environments. From a public health point of view, depressive disorders are the most significant mental health disorders that begin in adolescence. Using prospective and longitudinal design, this study aimed to increase the understanding of early-onset depressive disorders, related mental health disorders and developing substance use in a large population-derived sample of adolescent Finnish twins. The participants of this study, FinnTwin12, an ongoing longitudinal population-based study, came from Finnish families with twins born in 1983-87 (exhaustive of five birth cohorts, identified from Finland s Central Population Register). With follow-up ongoing at age 20-24, this thesis assessed adolescent mental health in the first three waves, starting from baseline age 11-12 to follow-ups at age 14 and 17½. Some 5600 twins participated in questionnaire assessments of a wide range of health related behaviors. Mental health was further assessed among an intensively studied subsample of 1852 adolescents, who completed also professionally administered interviews at age 14, which provided data for full DSM-IV/III-R (Diagnostic and Statistical Manual for Mental Health disorders, 4th and 3rd editions) diagnoses. The participation rates of the study were 87-92%. The results of the study suggest, that the diagnostic criteria for major depressive disorder (MDD) may not capture youth with clinically significant early-onset depressive conditions outside clinical settings. Milder cases of depression, namely adolescents fulfilling the diagnostic criteria for minor depressive disorder, a qualitatively similar condition to MDD with fewer symptoms are also associated with marked suicidal thoughts, plans and attempts, recurrences and a high degree of comorbidity. Prospectively and longitudinally, early-onset depressive disorders were of substantial importance in the context of other mental health disorders and substance use behaviors: These data from a large population-derived sample established a substantial overlap between early-onset depressive disorders and attention deficit hyperactivity disorder in adolescent females, both of them significantly predictive for development of substance use among girls. Only in females baseline DSM-IV ADHD symptoms were strong predictors of alcohol abuse and dependence and illicit drug use at age 14 and frequent alcohol use and illicit drug use at age 17.½ when conduct disorder and previous substance use were controlled for. Early-onset depressive disorders were also prospectively and longitudinally associated to daily smoking behavior, smokeless tobacco use, frequent alcohol use and illicit drug use and eating disorders. Analysis of discordant twins suggested that these predictive associations were independent of familial confounds, such as family income, structure and parental models. In sum, early-onset depressive disorders predict subsequent involvement of substance use and psychiatric morbidity. A heightened risk for substance use is substantial also among those depressed below categorical diagnosis of MDD. Whether early recognition and interventions among these young people hold potential for substance use prevention further in their lives has potential public health significance and calls for more research. Data from this population-derived sample with balanced representation of boys and girls, suggested that boys and girls with ADHD behaviors may differ from each other in their vulnerability to substance use and depressive disorders: the data suggest more adverse substance use outcome for girls that was not attenuated by conduct disorder or previous substance use. Further, the prospective associations of early-onset depressive disorders and future elevated levels of addictive substance use is not explained by familial factors supporting future substance use, which could have important implications for substance use prevention.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

The Pathobiology of the Saccular Cerebral Artery Aneurysm Rupture and Repair : A Clinicopathological and Experimental Approach

Backround and Purpose The often fatal (in 50-35%) subarachnoid hemorrhage (SAH) caused by saccular cerebral artery aneurysm (SCAA) rupture affects mainly the working aged population. The incidence of SAH is 10-11 / 100 000 in Western countries and twice as high in Finland and Japan. The estimated prevalence of SCAAs is around 2%. Many of those never rupture. Currently there are, however, no diagnostic methods to identify rupture-prone SCAAs from quiescent, (dormant) ones. Finding diagnostic markers for rupture-prone SCAAs is of primary importance since a SCAA rupture has such a sinister outcome, and all current treatment modalities are associated with morbidity and mortality. Also the therapies that prevent SCAA rupture need to be developed to as minimally invasive as possible. Although the clinical risk factors for SCAA rupture have been extensively studied and documented in large patient series, the cellular and molecular mechanisms how these risk factors lead to SCAA wall rupture remain incompletely known. Elucidation of the molecular and cellular pathobiology of the SCAA wall is needed in order to develop i) novel diagnostic tools that could identify rupture-prone SCAAs or patients at risk of SAH, and to ii) develop novel biological therapies that prevent SCAA wall rupture. Materials and Methods In this study, histological samples from unruptured and ruptured SCAAs and plasma samples from SCAA carriers were compared in order to identify structural changes, cell populations, growth factor receptors, or other molecular markers that would associate with SCAA wall rupture. In addition, experimental saccular aneurysm models and experimental models of mechanical vascular injury were used to study the cellular mechanisms of scar formation in the arterial wall, and the adaptation of the arterial wall to increased mechanical stress. Results and Interpretation Inflammation and degeneration of the SCAA wall, namely loss of mural cells and degradation of the wall matrix, were found to associate with rupture. Unruptured SCAA walls had structural resemblance with pads of myointimal hyperplasia or so called neointima that characterizes early atherosclerotic lesions, and is the repair and adaptation mechanism of the arterial wall after injury or increased mechanical stress. As in pads of myointimal hyperplasia elsewhere in the vasculature, oxidated LDL was found in the SCAA walls. Immunity against OxLDL was demonstrated in SAH patients with detection of circulating anti-oxidized LDL antibodies, which were significantly associated with the risk of rupture in patients with solitary SCAAs. Growth factor receptors associated with arterial wall remodeling and angiogenesis were more expressed in ruptured SCAA walls. In experimental saccular aneurysm models, capillary growth, arterial wall remodeling and neointima formation were found. The neointimal cells were shown to originate from the experimental aneurysm wall with minor contribution from the adjacent artery, and a negligible contribution of bone marrow-derived neointimal cells. Since loss of mural cells characterizes ruptured human SCAAs and likely impairs the adaptation and repair mechanism of ruptured or rupture-prone SCAAs, we investigated also the hypothesis that bone marrow-derived or circulating neointimal precursor cells could be used to enhance neointima formation and compensate the impaired repair capacity in ruptured SCAA walls. However, significant contribution of bone marrow cells or circulating mononuclear cells to neointima formation was not found.

Factors controlling the surface energy budget over snow and ice

Polar Regions are an energy sink of the Earth system, as the Sun rays do not reach the Poles for half of the year, and hit them only at very low angles for the other half of the year. In summer, solar radiation is the dominant energy source for the Polar areas, therefore even small changes in the surface albedo strongly affect the surface energy balance and, thus, the speed and amount of snow and ice melting. In winter, the main heat sources for the atmosphere are the cyclones approaching from lower latitudes, and the atmosphere-surface heat transfer takes place through turbulent mixing and longwave radiation, the latter dominated by clouds. The aim of this thesis is to improve the knowledge about the surface and atmospheric processes that control the surface energy budget over snow and ice, with particular focus on albedo during the spring and summer seasons, on horizontal advection of heat, cloud longwave forcing, and turbulent mixing during the winter season. The critical importance of a correct albedo representation in models is illustrated through the analysis of the causes for the errors in the surface and near-surface air temperature produced in a short-range numerical weather forecast by the HIRLAM model. Then, the daily and seasonal variability of snow and ice albedo have been examined by analysing field measurements of albedo, carried out in different environments. On the basis of the data analysis, simple albedo parameterizations have been derived, which can be implemented into thermodynamic sea ice models, as well as numerical weather prediction and climate models. Field measurements of radiation and turbulent fluxes over the Bay of Bothnia (Baltic Sea) also allowed examining the impact of a large albedo change during the melting season on surface energy and ice mass budgets. When high contrasts in surface albedo are present, as in the case of snow covered areas next to open water, the effect of the surface albedo heterogeneity on the downwelling solar irradiance under overcast condition is very significant, although it is usually not accounted for in single column radiative transfer calculations. To account for this effect, an effective albedo parameterization based on three-dimensional Monte Carlo radiative transfer calculations has been developed. To test a potentially relevant application of the effective albedo parameterization, its performance in the ground-based retrieval of cloud optical depth was illustrated. Finally, the factors causing the large variations of the surface and near-surface temperatures over the Central Arctic during winter were examined. The relative importance of cloud radiative forcing, turbulent mixing, and lateral heat advection on the Arctic surface temperature were quantified through the analysis of direct observations from Russian drifting ice stations, with the lateral heat advection calculated from reanalysis products.

Modeling Nonlinearities and Asymmetries in Asset Pricing

Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Reynolds stress and heat flux in spherical shell convection

Context. Turbulent fluxes of angular momentum and heat due to rotationally affected convection play a key role in determining differential rotation of stars. Aims. We compute turbulent angular momentum and heat transport as functions of the rotation rate from stratified convection. We compare results from spherical and Cartesian models in the same parameter regime in order to study whether restricted geometry introduces artefacts into the results. Methods. We employ direct numerical simulations of turbulent convection in spherical and Cartesian geometries. In order to alleviate the computational cost in the spherical runs and to reach as high spatial resolution as possible, we model only parts of the latitude and longitude. The rotational influence, measured by the Coriolis number or inverse Rossby number, is varied from zero to roughly seven, which is the regime that is likely to be realised in the solar convection zone. Cartesian simulations are performed in overlapping parameter regimes. Results. For slow rotation we find that the radial and latitudinal turbulent angular momentum fluxes are directed inward and equatorward, respectively. In the rapid rotation regime the radial flux changes sign in accordance with earlier numerical results, but in contradiction with theory. The latitudinal flux remains mostly equatorward and develops a maximum close to the equator. In Cartesian simulations this peak can be explained by the strong 'banana cells'. Their effect in the spherical case does not appear to be as large. The latitudinal heat flux is mostly equatorward for slow rotation but changes sign for rapid rotation. Longitudinal heat flux is always in the retrograde direction. The rotation profiles vary from anti-solar (slow equator) for slow and intermediate rotation to solar-like (fast equator) for rapid rotation. The solar-like profiles are dominated by the Taylor-Proudman balance.

FIN-CLARIN: A Framework for Depositing and Disseminating Language Resources for R&D

Researchers and developers in academia and industry would benefit from a facility that enables them to easily locate, licence and use the kind of empirical data they need for testing and refining their hypotheses and to deposit and disseminate their data e.g. to support replication and validation of reported scientific experiments. To answer these needs initially in Finland, there is an ongoing project at University of Helsinki and its collaborators to create a user-friendly web service for researchers and developers in Finland and other countries. In our talk, we describe ongoing work to create a palette of extensive but easily available Finnish language resources and technologies for the research community, including lexical resources, wordnets, morphologically tagged corpora, dependency syntactic treebanks and parsebanks, open-source finite state toolkits and libraries and language models to support text analysis and processing at customer site. Also first publicly available results are presented.