title: Shrinkage Estimation of Proportion via Logit Penalty

authors: Jung, Yoonsuh

subjects: Biased estimator

abstract: By releasing the unbiasedness condition, we often obtain more accurate estimators due to the bias-variance tradeoff. In this paper, we propose a class of shrinkage proportion estimators which show improved performance over the sample proportion. We provide the “optimal” amount of shrinkage. The advantage of the proposed estimators is given theoretically as well as explored empirically by simulation studies and real data analyses.

text: Shrinkage Estimation of Proportion via Logit Penalty By releasing the unbiasedness condition, we often obtain more accurate estimators due to the bias-variance tradeoff. In this paper, we propose a class of shrinkage proportion estimators which show improved performance over the sample proportion. We provide the “optimal” amount of shrinkage. The advantage of the proposed estimators is given theoretically as well as explored empirically by simulation studies and real data analyses.

year: 2016-04-05

title: Barlow Twins for semi-supervised learning in NIR spectroscopy

authors: Wohlers, Mark W.

subjects: Barlow Twins

abstract: Near-infrared (NIR) spectroscopy is a widely used technology in the horticulture industry for non-destructive fruit grading. Partial Least Squares (PLS) regression is the dominant method for producing fruit quality predictions from measured spectra. Alternative deep learning methods have shown promise, but often require large amounts of labelled data to train. This study proposes a semi-supervised method based on Barlow Twins to include unlabelled data in the training process. We adopt the Barlow Twins method by using repeated measurements on the same fruit from different devices as different “views” to encode into the same latent space and combine the encoder network with a regression head for prediction. Our approach demonstrates improved performance over PLS with up to 17% lower RMSE, especially when the labelled data is limited. The Barlow loss function also improves calibration transfer results.

text: Barlow Twins for semi-supervised learning in NIR spectroscopy Near-infrared (NIR) spectroscopy is a widely used technology in the horticulture industry for non-destructive fruit grading. Partial Least Squares (PLS) regression is the dominant method for producing fruit quality predictions from measured spectra. Alternative deep learning methods have shown promise, but often require large amounts of labelled data to train. This study proposes a semi-supervised method based on Barlow Twins to include unlabelled data in the training process. We adopt the Barlow Twins method by using repeated measurements on the same fruit from different devices as different “views” to encode into the same latent space and combine the encoder network with a regression head for prediction. Our approach demonstrates improved performance over PLS with up to 17% lower RMSE, especially when the labelled data is limited. The Barlow loss function also improves calibration transfer results.

year: 2026-02-15

title: The use of Māori words in National Science Challenge online discourse

authors: Calude, Andreea S.

abstract: This paper presents data relating to the use of Maori borrowings as they occur in a corpus of scientific discourse on the websites of the eleven National Science Challenges (NSCs) and their associated Twitter feeds.

text: The use of Māori words in National Science Challenge online discourse This paper presents data relating to the use of Maori borrowings as they occur in a corpus of scientific discourse on the websites of the eleven National Science Challenges (NSCs) and their associated Twitter feeds.

year: 2018