Previous seminars 2025

Spring

Seminar 2025-01-29: Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict

Speaker David Randahl, Department of Peace and Conflict Research, Uppsala University

Topic Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict

Abstract Armed conflict forecasting is an important area of research that has the potential to save lives and prevent suffering. However, most existing forecasting models provide only point predictions without any individual-level uncertainty estimates. In this paper, we propose a novel extension to conformal prediction which allows users to obtain individual-level prediction intervals for any arbitrary prediction model and which maintains a user-specified level of coverage across bins of values defined by the user. We then apply this bin-conditional conformal prediction model to forecast fatalities from armed conflict. The results show that the bin-conditional conformal prediction model provides well-calibrated uncertainty estimates for the predicted number of fatalities. The bin-conditional method outperforms the standard conformal prediction method with respect to thecalibration of coverage rates across different values of the prediction outcome, but at the cost of wider prediction intervals.

Seminar 2025-02-05: Legal and Ethical Implications of Transformer-Assisted Hate Crime Classification and Estimation in Sweden

Speaker Hannes Waldetoft, Department of Statistics at Uppsala University

Topic Legal and Ethical Implications of Transformer-Assisted Hate Crime Classification and Estimation in Sweden

Abstract Hate crimes, driven by biases against specific demographic groups, harm not only individuals but undermine the security, trust, and cohesion of entire communities. Accurately identifying such crimes remains a significant challenge due to under-reporting, limited training, and the complexity of determining bias motivations. In this paper, we analyze the results of a transformer-based classification model developed to improve the precision of hate crime statistics and identification in Sweden. Empirical results indicate the model outperforms traditional manual police classification of hate crimes, achieving higher precision across various crime types and regions. We further disaggregate performance to pinpoint persistent challenges and highlight categories where both human and machine decision-makers struggle. While the model focuses on statistical estimation rather than direct case-level decision-making, we discuss the broader implications of algorithmic transparency, accountability, and explainability. Ultimately, this research illustrates how transformer-based neural networks can responsibly bolster the detection and understanding of hate crimes, informing policies to better protect vulnerable communities

Seminar 2025-02-26: Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?

Speaker Luca Margaritella, Department of Economics, Lund University

Topic Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?

Abstract We propose a novel approach to estimate high-dimensional global bank network connectedness in both the time and frequency domains. By employing a factor model with sparse VAR idiosyncratic components, we decompose system-wide connectedness (SWC) into two key drivers: (i) common component shocks and (ii) idiosyncratic shocks. We also provide bootstrap confidence bands for all SWC measures. Furthermore, spectral density estimation allows us to disentangle SWC into short-, medium-, and long-term frequency responses to these shocks. We apply our methodology to two datasets of daily stock price volatilities for over 90 global banks, spanning the periods 2003-2013 and 2014-2023. Our empirical analysis reveals that SWC spikes during global crises, primarily driven by common component shocks and their short-term effects. Conversely, in normal times, SWC is largely influenced by idiosyncratic shocks and medium-term dynamics.

Seminar 2025-03-05: Are people with chronic pain more diverse than we think? An investigation of ergodicity

Speaker Felicia Sundström, Department of Psychology, Uppsala University

Topic Are people with chronic pain more diverse than we think? An investigation of ergodicity

Abstract This study investigates whether data from people with endometriosis (n = 58) and fibromyalgia (n = 58) exhibit what is called “ergodicity,” meaning that results from analyses of aggregated group data can be used to support conclusions about the individuals within the groups. The variables studied here are commonly investigated in chronic pain: pain intensity, pain interference, depressive symptoms, psychological flexibility, and pain catastrophizing. Data were collected twice daily for 42 days from each participant and analyzed in two ways: as separate cross-sectional group studies using the timepoints as the separate data sets (between-person) and as individual longitudinal studies using each person's time series data (within person). To confirm ergodicity, the results from the two analyses should agree. However, this is not what was observed in several respects. The between-person data showed substantially less variability compared with within-person data. This was evident in both the summary statistics involving single variables and in the correlational analyses. Overall, between-person correlations were relatively restricted in range, while within-person correlations varied widely. These findings have potentially profound implications for the field of chronic pain research. Because ergodicity was not found, this raises doubts around the assumption that aggregated data collected from groups can accurately represent the range of individual experiences in chronic pain. The results advocate for a shift toward inclusion of more individual person-focused approaches as an addition to group-based approaches. This shift could lead to more personalized and effective treatments by better capturing and then clarifying the heterogeneous nature of chronic pain, including the processes that underlie it.

Seminar 2025-03-12: Velocities of moving random surfaces

Speaker Krzysztof Podgórski, Department of Statistics, Lund University

Topic Velocities of moving random surfaces

Abstract For a stationary two-dimensional random field evolving in time, one can derive statistical distributions of appropriately defined velocities utilizing a generalization of the Rice formula. The theory can be applied to practical problems where evolving random fields are considered to be adequate models. Examples include changes of atmospheric pressure, variation of air pollution, or dynamical models of the sea surface elevation. In particular, statistical properties of velocities can be obtained both for the sea surface and for the envelope field based on this surface. Additional extension can be obtained by studying three-dimensional geometry of spatial waves. Their statistical distributions can be presented in explicit integral forms for the deep water seas modeled as Gaussian fields. The proposed approach allows for investigation of the effect that shape and directionality of the sea spectrum have on the joint distributions of the size characteristics.

Seminar 2025-03-19: Testable implications of outcome-independent MNAR

Speaker Arvid Sjölander, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet

Topic Testable implications of outcome-independent MNAR

Abstract The standard taxonomy for missing data analysis separates missingness mechanisms into “missing completely at random” (MCAR), “missing at random” (MAR) and “missing not at random” (MNAR). Whereas multiple imputation requires MAR for unbiasedness, it is often argued that the simpler complete-case analysis requires the stronger condition MCAR. In this presentation, we will show that a complete-case analysis can be unbiased under a realistic special case of MNAR, which we label outcome-independent MNAR, and we show that multiple imputation is generally biased under this missingness mechanism. This challenges the common assertion that multiple imputation is always preferable to a complete-case analysis, from a bias perspective. We further show that the assumption of outcome independent MNAR can be tested with data. This stands in contrast to MAR, which is fundamentally untestable.

Seminar 2025-03-26: A General Design-Based Framework and Estimator for Randomized Experiments

Speaker Fredrik Sävje, Department of Economics, Uppsala University

Topic A General Design-Based Framework and Estimator for Randomized Experiments

Abstract We describe a new design-based framework for drawing causal inference in randomized experiments. Causal effects in the framework are defined as linear functionals evaluated at potential outcome functions. Knowledge and assumptions about the potential outcome functions are encoded as function spaces. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions. We describe a class of estimators for estimands defined using the framework and investigate their properties. The construction of the estimators is based on the Riesz representation theorem. We provide necessary and sufficient conditions for unbiasedness and consistency. Finally, we provide conditions under which the estimators are asymptotically normal, and describe a conservative variance estimator to facilitate the construction of confidence intervals for the estimands

Seminar 2025-04-02: Preliminary estimation using the LM-principle

Speker Johan Lyhagen, Department of Statistics at Uppsala University

Topic Preliminary estimation using the LM-principle

Abstract Thematic theories are incomplete meaning that they do not give a full description of how to specify a model. Rather they focus on certain aspects of interest which means that there are parts of the model that needs to be empirically decided. This includes lag-lengths in time series analysis, factor structures and correlations amongst errors in SEM, or control variables in causal inference (sensitivity analysis). In SEM there are modification indices, mainly for the purpose of improving the fit of the model, that estimate the increase of the likelihood when relaxing a restriction. Subsequently, one can also derive an estimated parameter change when relaxing a restriction. In this paper we generalise this to relaxing more than one parameter, focusing on the estimated parameter change in the parameters of interest, and derive this in the GLM setting as well as in the traditional SEM. The paper includes theoretical results, Monte Carlo simulations to investigate the small sample properties and empirical examples to show the usefulness for empirical researchers.

Seminar 2025-04-09: Supervised learning for repeated measures data

Speaker Martin Singull, Department of Mathematics, Linköping University

Topic Supervised learning for repeated measures data

Abstract Multivariate repeated measures data, which correspond to multiple measurements that are taken over time on each unit or subject, are common in various applications such as medicine, pharmacy, environmental research, engineering, business, finance, etc. In this presentation we will discuss supervised learning, i.e., model fitting and classification, of repeated measurements following a Growth Curve model, which is also known as a bilinear regression model. In the end of the presentation we will also consider some real data example.