Analysis of a dose-response assay in Scaptotrigona bipunctata bees, Lepeletier, 1836 (Hymenoptera: Apidae) using the logistic regression model under the Bayesian approach

This paper shows the results of a dose-response study in Scaptotrigona bipunctata bees, Lepeletier, 1836 (Hymenoptera: Apidae) exposed to the insecticide Fastac Duo. The aim was to evaluate the lethal concentration that causes the death of 50% of bees (LC50) and investigate the odd of mortality after exposure to different concentrations, using the logistic regression model under the Bayesian approach. In this approach, it is possible to incorporate a prior information and gives more accurate inferential results. Three independent dose-response experiments were analyzed, dissimilar in their lead time according to guidelines from the Organisation for Economic Co-operation and Development (OECD), in which each assay contained four replicates at the concentration levels investigated, including control. Observing exposure to the agrochemical, it was identified that the higher the concentration, the greater the odd of mortality. Regarding the estimated lethal concentrations for each experiment, the following values were found, 0.03 g a.i. L-1, for 24 hours, 0.04 g a.i. L-1, for 48 hours and 0.06 g a.i. L-1 for 72 hours, showing that in experiments with longer exposure times there was an increase in LC50. Concluding, the study showed an alternative approach to classical methods for dose-response studies in Scaptotrigona bipunctata bees exposed to the insecticide Fastac Duo.

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Evaluation of the financial condition of companies after the announcement of arrangement bankruptcy: application of the classical and Bayesian logistic regression

Przegląd Statystyczny ◽

10.5604/01.3001.0014.1782 ◽

2020 ◽

Vol 67 (1) ◽

pp. 5-32

Author(s):

Barbara Pawełek ◽

Jadwiga Kostrzewska ◽

Maciej Kostrzewski ◽

Krzysztof Gałuszka

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Construction Industry ◽

Bayesian Approach ◽

Logistic Regression Model ◽

Likelihood Method ◽

Logistic Regression Models ◽

Financial Condition ◽

Bayesian Logistic Regression ◽

The Bayesian Approach

The aim of this paper is to present the results of an assessment of the financial condition of companies from the construction industry after the announcement of arrangement bankruptcy, in comparison to the condition of healthy companies. The logistic regression model estimated by means of the maximum likelihood method and the Bayesian approach were used. The first achievement of our study is the assessment of the financial condition of companies from the construction industry after the announcement of bankruptcy. The second achievement is the application of an approach combining the classical and Bayesian logistic regression models to assess the financial condition of companies in the years following the declaration of bankruptcy, and the presentation of the benefits of such a combination. The analysis described in the paper, carried out in most part by means of the ML logistic regression model, was supplemented with information yielded by the application of the Bayesian approach. In particular, the analysis of the shape of the posterior distribution of the repeat bankruptcy probability makes it possible, in some cases, to observe that the financial condition of a company is not clear, despite clear assessments made on the basis of the point estimations.

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Increased influence of prior choices on perceptual decisions in autism

eLife ◽

10.7554/elife.61595 ◽

2021 ◽

Vol 10 ◽

Author(s):

Helen Feigin ◽

Shir Shalom-Sperber ◽

Ditza A Zachor ◽

Adam Zaidel

Keyword(s):

Autism Spectrum Disorder ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Prior Information ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Sensory Stimuli ◽

Specific Effects

Autism spectrum disorder (ASD) manifests sensory and perceptual atypicalities. Recent theories suggest that these may reflect a reduced influence of prior information in ASD. Some studies have found reduced adaptation to recent sensory stimuli in ASD. However, the effects of prior stimuli and prior perceptual choices can counteract one-another. Here, we investigated this using two different tasks (in two different cohorts): (i) visual location discrimination, and (ii) multisensory (visual-vestibular) heading discrimination. We fit the data using a logistic regression model to dissociate the specific effects of prior stimuli and prior choices. In both tasks perceptual decisions were biased toward recent choices. Notably, the 'attractive' effect of prior choices was significantly larger in ASD (in both tasks and cohorts), while there was no difference in the influence of prior stimuli. These results challenge theories of reduced priors in ASD, and rather suggest an increased consistency bias for perceptual decisions in ASD.

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Inferences About the Probability of Success, Given the Value of a Covariate, Using a Nonparametric Smoother

Journal of Modern Applied Statistical Methods ◽

10.22237/jmasm/1556670240 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Rand Wilcox

Keyword(s):

Logistic Regression ◽

Confidence Interval ◽

Regression Model ◽

Goodness Of Fit ◽

Logistic Regression Model ◽

Random Variable ◽

Goodness Of Fit Test ◽

Binary Random Variable ◽

Alternative Approach ◽

Probability Of Success

For a binary random variable Y, let p(x) = P(Y = 1 | X = x) for some covariate X. The goal of computing a confidence interval for p(x) is considered. In the logistic regression model, even a slight departure difficult to detect via a goodness-of-fit test can yield inaccurate results. The accuracy of a confidence interval can deteriorate as the sample size increases. The goal is to suggest an alternative approach based on a smoother, which provides a more flexible approximation of p(x).

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Increased influence of prior choices on perceptual decisions in autism

10.1101/2020.07.28.224808 ◽

2020 ◽

Author(s):

Helen Feigin ◽

Shir Shalom-Sperber ◽

Ditza A. Zachor ◽

Adam Zaidel

Keyword(s):

Autism Spectrum Disorder ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Prior Information ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Sensory Stimuli ◽

Specific Effects

ABSTRACTAutism spectrum disorder (ASD) manifests sensory and perceptual atypicalities. Recent theories suggest that these may reflect a reduced influence of prior information in ASD. Some studies have found reduced adaptation to recent sensory stimuli in ASD. However, the effects of prior stimuli and prior perceptual choices can counteract one-another. Here, we investigated this using two different tasks (in two different cohorts): (i) visual location discrimination, and (ii) multisensory (visual-vestibular) heading discrimination. We fit the data using a logistic regression model to dissociate the specific effects of prior stimuli and prior choices. In both tasks perceptual decisions were biased toward recent choices. Notably, the ‘attractive’ effect of prior choices was significantly larger in ASD (in both tasks and cohorts), while there was no difference in the influence of prior stimuli. These results challenge theories of reduced priors in ASD, and rather suggest an increased consistency bias for perceptual decisions in ASD.

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