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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest ; Quinn, Anthony
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented.
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Risk-Sensitivity and Average Optimality in Markov and Semi-Markov Reward Processes
Sladký, Karel
This contribution is devoted to risk-sensitivity in long-run average optimality of Markov and semi-Markov reward processes. Since the traditional average optimality criteria cannot reflect the variability-risk features of the problem, we are interested in more sophisticated approaches where the stream of rewards generated by the Markov chain that is evaluated by an exponential utility function with a given risk sensitivity coefficient. Recall that for the risk sensitivity coefficient equal to zero (i.e. the so called risk-neutral case) we arrive at traditional optimality criteria, if the risk sensitivity coefficient is close to zero the Taylor expansion enables to evaluate variability of the generated total reward. Observe that the first moment of the total reward corresponds to expectation of total reward and the second central moment to the reward variance. In this note we present necessary and sufficient risk-sensitivity and risk-neutral optimality conditions for long run risk-sensitive average optimality criterion of unichain Markov and semi-Markov reward processes.
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A Note on Stochastic Optimization Problems with Nonlinear Dependence on a Probability Measure
Kaňková, Vlasta
Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic optimization rather often. Namely, the corresponding type of problems corresponds to many situations in applications. The nonlinear dependence can appear as in the objective functions so in a constraints set. We plan to consider the case of static (one-objective) problems in which nonlinear dependence appears in the objective function with a few types of constraints sets. In details we consider constraints sets “deterministic”, depending nonlinearly on the probability measure, constraints set determined by second order stochastic dominance and the sets given by mean-risk problems. The last case means that the constraints set corresponds to solutions those guarantee an acceptable value in both criteria. To introduce corresponding assertions we employ the stability results based on the Wasserstein metric and L1 norm. Moreover, we try to deal also with the case when all results have to be obtained (estimated) on the data base.
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Use of the BCC and Range Directional DEA Models within an Efficiency Evaluation
Houda, Michal
The contribution deals with two data envelopment analysis (DEA) models, in particular the BCC model (radial DEA model with variable returns to scale), and the range directional model. The mathematical description of the models are provided and several properties reported. A numerical comparison of the two models on real industrial data is provided with discussion about possible drawbacks of simplifying modeling procedures.
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Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach
Švorc, Jan ; Vomlel, Jiří
The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared\nwith Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions.
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Selective Attention in Exchange Rate Forecasting
Kapounek, S. ; Kučerová, Z. ; Kočenda, Evžen
We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.
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Financial Crime and Punishment: A Meta-Analysis
de Batz, L. ; Kočenda, Evžen
We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. Specifically, we conduct a meta-regression analysis and investigate the extent and nature of the impact that the publication of financial misconducts exerts on stock returns. We survey 111 studies, published between 1978 and 2020, with a total of 439 estimates from event studies. Our key finding is that the average abnormal returns calculated from this empirical literature are affected by a negative publication selection bias. Still, after controlling for this bias, our meta-analysis indicates that publications of financial crimes are followed by statistically significant negative abnormal returns, which suggests the existence of an informational effect. Finally, the MRA results demonstrate that crimes committed in common law countries, alleged crimes, and accounting crimes carry particularly weighty information for market participants. The results call for more transparency on side of enforcers along enforcement procedures, to foster timely and proportionate market reactions and support efficient markets.
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