| 1. |
- Berhe, Leakemariam, 1960-
(författare)
-
Statistical modeling and design in forestry : The case of single tree models
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Forest quantification methods have evolved from a simple graphical approach to complex regression models with stochastic structural components. Currently, mixed effects models methodology is receiving attention in the forestry literature. However, the review work (Paper I) indicates a tendency to overlook appropriate covariance structures in the NLME modeling process.A nonlinear mixed effects modeling process is demonstrated in Paper II using Cupressus lustanica tree merchantable volume data and compared several models with and without covariance structures. For simplicity and clarity of the nonlinear mixed effects modeling, four phases of modeling were introduced. The nonlinear mixed effects model for C. lustanica tree merchantable volume with the covariance structures for both the random effects and within group errors has shown a significant improvement over the model with simplified covariance matrix. However, this statistical significance has little to explain in the prediction performance of the model.In Paper III, using several performance indicator statistics, tree taper models were compared in an effort to propose the best model for the forest management and planning purpose of the C. lustanica plantations. Kozak's (1988) tree taper model was found to be the best for estimating C. lustanica taper profile.Based on the Kozak (1988) tree taper model, a Ds optimal experimental design study is carried out in Paper IV. In this study, a Ds-optimal (sub) replication free design is suggested for the Kozak (1988) tree taper model.
|
|
| 2. |
- Angelov, Angel G., 1983-
(författare)
-
Methods for interval-censored data and testing for stochastic dominance
- 2018
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis includes four papers: the first three of them are concerned with methods for interval-censored data, while the forth paper is devoted to testing for stochastic dominance.In many studies, the variable of interest is observed to lie within an interval instead of being observed exactly, i.e., each observation is an interval and not a single value. This type of data is known as interval-censored. It may arise in questionnaire-based studies when the respondent gives an answer in the form of an interval without having pre-specified ranges. Such data are called self-selected interval data. In this context, the assumption of noninformative censoring is not fulfilled, and therefore the existing methods for interval-censored data are not necessarily applicable.A problem of interest is to estimate the underlying distribution function. There are two main approaches to this problem: (i) parametric estimation, which assumes a particular functional form of the distribution, and (ii) nonparametric estimation, which does not rely on any distributional assumptions. In Paper A, a nonparametric maximum likelihood estimator for self-selected interval data is proposed and its consistency is shown. Paper B suggests a parametric maximum likelihood estimator. The consistency and asymptotic normality of the estimator are proven.Another interesting problem is to infer whether two samples arise from identical distributions. In Paper C, nonparametric two-sample tests suitable for self-selected interval data are suggested and their properties are investigated through simulations.Paper D concerns testing for stochastic dominance with uncensored data. The paper explores a testing problem which involves four hypotheses, that is, based on observations of two random variables X and Y, one wants to discriminate between four possibilities: identical survival functions, stochastic dominance of X over Y, stochastic dominance of Y over X, or crossing survival functions. Permutation-based tests suitable for two independent samples and for paired samples are proposed. The tests are applied to data from an experiment concerning the individual's willingness to pay for a given environmental improvement.
|
|
| 3. |
- Barros, Guilherme, 1992-
(författare)
-
Estimation of hazard ratios from observational data with applications related to stroke
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The objective of this thesis is to examine some challenges that may emerge when conducting time-to-event studies based on observational data. Time-to-event (also called survival) is a setting that involves analyzing how different factors may influence the length of time until an individual experiences the event of interest. This type of analysis is commonly applied in fields such as medical research and epidemiology. In this thesis, which focuses on stroke, we are interested in the time to a recurrent stroke or the death of a patient who survived a first stroke.Hazard ratios are one of the main parameters estimated in time-to-event studies. Hazard ratios involve comparing the risk of experiencing the event between two groups, usually a treated group and an untreated group. They can also involve other factors, such as different age groups. Hazard ratios can be estimated from the data by using the Cox regression model.Observational data, in contrast to experimental data, involves data collected without any intervention or random assignment of treatment to the individuals. Confounders, that is, variables that distort or obscure the true relationship between treatment and outcome, are always present and need to be controlled for in observational studies.National registers are an important source of observational data. A national registry is a centralized database or system that collects, stores, and maintains information about a specific population or group of individuals within a country. Sweden is known for its detailed and complete national registers. In this thesis, data from the Swedish Stroke Register (Riksstroke) is used to study factors related to stroke.In time-to-event studies involving observational data, several challenges may arise for the researcher during data analysis. Some individuals may not experience the event during the observation period and thus the information about their time until the event is incomplete. These individuals are considered as censored. Some individuals may experience another event rather than the one of interest, a competing risk. Additionally, models must be properly constructed, with researchers selecting variables and determining the suitable functional form.Four papers are included in the thesis. Paper I demonstrates how to handle competing risks in survival analysis. The study involves comparing individuals with and without standard modifiable risk factors and their risks of a recurrent stroke or death using data from the Swedish Stroke Register.The estimation of marginal hazard ratios is a common theme in the other three papers. All involve simulation studies in order to extend methods and explore best practices when estimating marginal hazard ratios.Paper II explores non-parametric methods that can be used as alternatives to more traditional parametric methods when balancing datasets in order to estimate a marginal hazard ratio. A case study was also conducted using data from the Swedish Stroke Register involving the prescription of anticoagulants at hospital discharge after a stroke.Paper III is about how censoring affects marginal hazard ratio estimation, even with perfect balancing of the dataset. We study this issue, taking into consideration varying effect sizes and censoring rates. A procedure to attenuate the problem is also studied.Paper IV concerns covariate selection in the case of high-dimensional data. High-dimensional data involves cases in which the number of covariates in the study is comparable to the number of individuals, and therefore covariate selection methods are needed. In the paper, we explore some of these methods and suggest a best-performing procedure. As Paper II, Paper IV involves a case study of anticoagulant prescription using data from the Swedish Stroke Register.
|
|
| 4. |
- Bränberg, Kenny, 1956-
(författare)
-
Observed score equating with covariates
- 2010
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In test score equating the focus is on the problem of finding the relationship between the scales of different test forms. This can be done only if data are collected in such a way that the effect of differences in ability between groups taking different test forms can be separated from the effect of differences in test form difficulty. In standard equating procedures this problem has been solved by using common examinees or common items. With common examinees, as in the equivalent groups design, the single group design, and the counterbalanced design, the examinees taking the test forms are either exactly the same, i.e., each examinee takes both test forms, or random samples from the same population. Common items (anchor items) are usually used when the samples taking the different test forms are assumed to come from different populations. The thesis consists of four papers and the main theme in three of these papers is the use of covariates, i.e., background variables correlated with the test scores, in observed score equating. We show how covariates can be used to adjust for systematic differences between samples in a non-equivalent groups design when there are no anchor items. We also show how covariates can be used to decrease the equating error in an equivalent groups design or in a non-equivalent groups design. The first paper, Paper I, is the only paper where the focus is on something else than the incorporation of covariates in equating. The paper is an introduction to test score equating, and the author's thoughts on the foundation of test score equating. There are a number of different definitions of test score equating in the literature. Some of these definitions are presented and the similarities and differences between them are discussed. An attempt is also made to clarify the connection between the definitions and the most commonly used equating functions. In Paper II a model is proposed for observed score linear equating with background variables. The idea presented in the paper is to adjust for systematic differences in ability between groups in a non-equivalent groups design by using information from background variables correlated with the observed test scores. It is assumed that conditional on the background variables the two samples can be seen as random samples from the same population. The background variables are used to explain the systematic differences in ability between the populations. The proposed model consists of a linear regression model connecting the observed scores with the background variables and a linear equating function connecting observed scores on one test forms to observed scores on the other test form. Maximum likelihood estimators of the model parameters are derived, using an assumption of normally distributed test scores, and data from two administrations of the Swedish Scholastic Assessment Test are used to illustrate the use of the model. In Paper III we use the model presented in Paper II with two different data collection designs: the non-equivalent groups design (with and without anchor items) and the equivalent groups design. Simulated data are used to examine the effect - in terms of bias, variance and mean squared error - on the estimators, of including covariates. With the equivalent groups design the results show that using covariates can increase the accuracy of the equating. With the non-equivalent groups design the results show that using an anchor test together with covariates is the most efficient way of reducing the mean squared error of the estimators. Furthermore, with no anchor test, the background variables can be used to adjust for the systematic differences between the populations and produce unbiased estimators of the equating relationship, provided that the “right” variables are used, i.e., the variables explaining those differences. In Paper IV we explore the idea of using covariates as a substitute for an anchor test with a non-equivalent groups design in the framework of Kernel Equating. Kernel Equating can be seen as a method including five different steps: presmoothing, estimation of score probabilities, continuization, equating, and calculating the standard error of equating. For each of these steps we give the theoretical results when observations on covariates are used as a substitute for scores on an anchor test. It is shown that we can use the method developed for Post-Stratification Equating in the non-equivalent groups with anchor test design, but with observations on the covariates instead of scores on an anchor test. The method is illustrated using data from the Swedish Scholastic Assessment Test.
|
|
| 5. |
- Danardono, , 1968-
(författare)
-
Multiple Time Scales and Longitudinal Measurements in Event History Analysis
- 2005
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A general time-to-event data analysis known as event history analysis is considered. The focus is on the analysis of time-to-event data using Cox's regression model when the time to the event may be measured from different origins giving several observable time scales and when longitudinal measurements are involved. For the multiple time scales problem, procedures to choose a basic time scale in Cox's regression model are proposed. The connections between piecewise constant hazards, time-dependent covariates and time-dependent strata in the dual time scales are discussed. For the longitudinal measurements problem, four methods known in the literature together with two proposed methods are compared. All quantitative comparisons are performed by means of simulations. Applications to the analysis of infant mortality, morbidity, and growth are provided.
|
|
| 6. |
- Ecker, Kreske, 1987-
(författare)
-
Studying earnings trajectories as functional outcomes
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we present methods for studying patterns of income accumulation over time using functional data analysis. This is made possible by the availability of large-scale longitudinal register data in Sweden. By modelling individuals’ cumulative earnings trajectories as continuous functions of time, we can explore temporal dynamics as well as divergences in these trajectories based on initial labour market conditions. A major contribution of this thesis consists of extending the potential outcome framework for causal inference to functional data analysis.In Paper I, we use functional-on-scalar linear regression and an interval-wise testing procedure to study the associations between initial labour market size and income trajectories for one Swedish birth cohort. In Paper II, we present methods to draw causal conclusions in this setting. We introduce the functional average treatment effect (FATE), as well as an outcome-regression based estimator for this parameter. In addition, we show the finite sample distribution of this estimator under certain regularity conditions and demonstrate how simultaneous confidence bands can be used for inferences about the FATE. An application study in this paper estimates the causal effect of initial labour market size on income accumulation trajectories.In Paper III, these methods are applied to study the effect of initial firm age on earnings accumulation. Paper IV presents an outcome regression based and a double robust estimator for the mean of functional outcomes when some of these outcome functions are missing at random. We derive the asymptotic distributions of these two estimators as well as their covariance structure under more general conditions.
|
|
| 7. |
- Elezovic, Suad, 1965-
(författare)
-
Modeling financial volatility : A functional approach with applications to Swedish limit order book data
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is designed to offer an approach to modeling volatility in the Swedish limit order market. Realized quadratic variation is used as an estimator of the integrated variance, which is a measure of the variability of a stochastic process in continuous time. Moreover, a functional time series model for the realized quadratic variation is introduced. A two-step estimation procedure for such a model is then proposed. Some properties of the proposed two-step estimator are discussed and illustrated through an application to high-frequency financial data and simulated experiments. In Paper I, the concept of realized quadratic variation, obtained from the bid and ask curves, is presented. In particular, an application to the Swedish limit order book data is performed using signature plots to determine an optimal sampling frequency for the computations. The paper is the first study that introduces realized quadratic variation in a functional context. Paper II introduces functional time series models and apply them to the modeling of volatility in the Swedish limit order book. More precisely, a functional approach to the estimation of volatility dynamics of the spreads (differences between the bid and ask prices) is presented through a case study. For that purpose, a two-step procedure for the estimation of functional linear models is adapted to the estimation of a functional dynamic time series model. Paper III studies a two-step estimation procedure for the functional models introduced in Paper II. For that purpose, data is simulated using the Heston stochastic volatility model, thereby obtaining time series of realized quadratic variations as functions of relative quantities of shares. In the first step, a dynamic time series model is fitted to each time series. This results in a set of inefficient raw estimates of the coefficient functions. In the second step, the raw estimates are smoothed. The second step improves on the first step since it yields both smooth and more efficient estimates. In this simulation, the smooth estimates are shown to perform better in terms of mean squared error. Paper IV introduces an alternative to the two-step estimation procedure mentioned above. This is achieved by taking into account the correlation structure of the error terms obtained in the first step. The proposed estimator is based on seemingly unrelated regression representation. Then, a multivariate generalized least squares estimator is used in a first step and its smooth version in a second step. Some of the asymptotic properties of the resulting two-step procedure are discussed. The new procedure is illustrated with functional high-frequency financial data.
|
|
| 8. |
- Fahlén, Jessica, 1973-
(författare)
-
Essays on spatial point processes and bioinformatics
- 2010
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of two separate parts. The first part consists of one paper and considers problems concerning spatial point processes and the second part includes three papers in the field of bioinformatics. The first part of the thesis is based on a forestry problem of estimating the number of trees in a region by using the information in an aerial photo, showing the area covered by the trees. The positions of the trees are assumed to follow either a binomial point process or a hard-core Strauss process. Furthermore, discs of equal size are used to represent the tree-crowns. We provide formulas for the expectation and the variance of the relative vacancy for both processes. The formulas are approximate for the hard-core Strauss process. Simulations indicate that the approximations are accurate. The second part of this thesis focuses on pre-processing of microarray data. The microarray technology can be used to measure the expression of thousands of genes simultaneously in a single experiment. The technique is used to identify genes that are differentially expressed between two populations, e.g. diseased versus healthy individuals. This information can be used in several different ways, for example as diagnostic tools and in drug discovery. The microarray technique involves a number of complex experimental steps, where each step introduces variability in the data. Pre-processing aims to reduce this variation and is a crucial part of the data analysis. Paper II gives a review over several pre-processing methods. Spike-in data are used to describe how the different methods affect the sensitivity and bias of the experiment. An important step in pre-processing is dye-normalization. This normalization aims to remove the systematic differences due to the use of different dyes for coloring the samples. In Paper III a novel dye-normalization, the MC-normalization, is proposed. The idea behind this normalization is to let the channels’ individual intensities determine the correction, rather than the average intensity which is the case for the commonly used MA-normalization. Spike-in data showed that the MC-normalization reduced the bias for the differentially expressed genes compared to the MA-normalization. The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). In Paper IV we used tongue-cancer microRNA-microarray data to study the effect of FFPE-storage. We suggest that the microRNAs are not equally affected by the storage time and propose a novel procedure to remove this bias. The procedure improves the ability of the analysis to detect differentially expressed microRNAs.
|
|
| 9. |
- Fowler, Philip, 1986-
(författare)
-
Methods for improving covariate balance in observational studies
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment.The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias.Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects.The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.
|
|
| 10. |
- Genbäck, Minna, 1985-
(författare)
-
Uncertainty intervals and sensitivity analysis for missing data
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR.The thesis covers different types of linear regression. In Paper I and III we have a continuous outcome, in Paper II a binary outcome, and in Paper IV we allow for mixed effects with a continuous outcome. In Paper III we estimate the effect of a treatment, which can be seen as an example of missing outcome data.
|
|
