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Overview and evaluation of various frequentist test statistics using constrained statistical inference in the context of linear regression Ghent University
Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative test statistics exist that each come with different versions, strengths and weaknesses. This paper gives an overview about these statistics, including the Wald, the LRT, the Score, the (F) over ...
Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen) KU Leuven
Statistical relational models combine aspects of first-order logic and probabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in the model, and reasoning about ...
Multiply Robust Inference for Statistical Interactions Ghent University
A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the man interest lies in the interaction, this approach is not entirely satisfactory, because it is prone to (possibly severe) bias when ...
Towards Declarative Statistical Inference KU Leuven
Wide-ranging digitalization has made it possible to capture increasingly larger amounts of data. In order to transform this raw data into meaningful insights, data analytics and statistical inference techniques are essential. However, while it is expected that a researcher is an expert in their own field, it is not self-evident that they are also proficient in statistics. In fact, it is known that statistical inference is a labor-intensive and ...
Statistical inference and focused model selection for high-dimensional regression models. KU Leuven
The last few decades have seen a spectacular increase in the collection of high-dimensional data. Those data are characterized by a number of covariates larger than the sample size and can be found in various fields such as genetics, finance, ecology, health, and image processing. In many cases, we are interested in determining the influence of the set of covariates (e.g. age, sex, medical data, gene expressions) on a particular quantity of ...
The win odds: statistical inference and regression Hasselt University
Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We ...
Statistical inference for measures of predictive success KU Leuven
© 2015, The Author(s). We provide statistical inference for measures of predictive success. These measures are frequently used to evaluate and compare the performance of different models of individual and group decision making in experimental and revealed preference studies. We provide a brief illustration of our findings by comparing the predictive success of different revealed preference tests for models of intertemporal decision making. This ...
Statistical inference for the sparse parameter of a partially linear single-index model KU Leuven
We perform inference for the sparse and potentially high-dimensional parametric part of a partially linear single-index model. We construct a desparsified version of a penalized estimator for which asymptotic normality can be proven. This allows us to take the uncertainty associated with the variable selection process into account and to construct confidence intervals for all the components of the parameter.
Statistical parametric mapping of biomechanical one-dimensional data with Bayesian inference. Vrije Universiteit Brussel
Recent developments in Statistical Parametric Mapping (SPM) for continuum data (e.g. kinematic time series) have been adopted by the biomechanics research community with great interest. The Python/MATLAB package spm1d developed by T. Pataky has introduced SPM into the biomechanical literature, adapted originally from neuroimaging. The package already allows many of the statistical analyses common in biomechanics from a frequentist perspective. ...