18 research outputs found
Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap
The Nelson–Siegel (NS) model is widely used in practice to fit the term structure of interest rates largely due to its high efficacy in the in-sample fit and out-of-sample forecasting of bond yields. In this paper, we compare forecasting performances of estimated yields from the Nelson–Siegel-based models and some simpler time series models, using the daily, weekly, and monthly data during a prolong period of liquidity trap in Japan. We find that the out-of-sample expanding window forecasts by NS-based models in general perform less satisfactory than the competitor models. However, the NS-based models can be useful in forecasting yields over longer horizons and can work well with GARCH-type structures in modeling the conditional volatility
Vitamin D and cause-specific vascular disease and mortality:a Mendelian randomisation study involving 99,012 Chinese and 106,911 European adults
MODELLING DEPENDENCE IN STI COMPONENT STOCKS: AN ANALYSIS WITH R-VINE COPULA MODELS
Bachelor'sBachelor of Social Sciences (Honours
Handling Noisy Labels via One-Step Abductive Multi-Target Learning: An Application to Helicobacter Pylori Segmentation
Learning from noisy labels is an important concern because of the lack of
accurate ground-truth labels in plenty of real-world scenarios. In practice,
various approaches for this concern first make some corrections corresponding
to potentially noisy-labeled instances, and then update predictive model with
information of the made corrections. However, in specific areas, such as
medical histopathology whole slide image analysis (MHWSIA), it is often
difficult or even impossible for experts to manually achieve the noisy-free
ground-truth labels which leads to labels with complex noise. This situation
raises two more difficult problems: 1) the methodology of approaches making
corrections corresponding to potentially noisy-labeled instances has
limitations due to the complex noise existing in labels; and 2) the appropriate
evaluation strategy for validation/testing is unclear because of the great
difficulty in collecting the noisy-free ground-truth labels. In this paper, we
focus on alleviating these two problems. For the problem 1), we present
one-step abductive multi-target learning (OSAMTL) that imposes a one-step
logical reasoning upon machine learning via a multi-target learning procedure
to constrain the predictions of the learning model to be subject to our prior
knowledge about the true target. For the problem 2), we propose a logical
assessment formula (LAF) that evaluates the logical rationality of the outputs
of an approach by estimating the consistencies between the predictions of the
learning model and the logical facts narrated from the results of the one-step
logical reasoning of OSAMTL. Applying OSAMTL and LAF to the Helicobacter pylori
(H. pylori) segmentation task in MHWSIA, we show that OSAMTL is able to enable
the machine learning model achieving logically more rational predictions, which
is beyond various state-of-the-art approaches in handling complex noisy labels.Comment: 71 pages, 39 figure