Haphazard Sampling: Selection Biases and the Estimation Consequences of These Biases

Hall, Thomas W.; Higson, Andrew; Pierce, Bethane Jo; Price, Kenneth H.; Skousen, Christopher J.

doi:10.2308/ciia-50568

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…The two cases of epimeletic behavior were recorded as part of an intensive research program on common bottlenose dolphins run by the Department of Environmental Biology (DBA) of “La Sapienza” University of Rome since 2017 in the Roman seas. The collection of acoustic and visual data was carried out during daily surveys using a sailing vessel, Beneteau Oceanis 41.1 powered by a 55 hp Volvo diesel engine, in suitable weather conditions (i.e., sea state < 3 Douglas, wind force Beaufort < 3, no rain, no fog), at a steady speed of 4–6 knots, following non-systematic haphazard sampling procedure [ 24 ]. Acoustic data were collected using one towed hydrophone Aquarian Audio (Anacortes, WA, USA, model H1c-2018 provided by Nauta srl; sensitivity −199 dB re 1 V/μPa; flat frequency response from 20 Hz to 4 kHz ± 4 dB with a bandwidth between <0.1 to >100 kHz) and one digital sound card Roland Quad Capture.…”

Section: Methodsmentioning

confidence: 99%

First Report of Epimeletic and Acoustic Behavior in Mediterranean Common Bottlenose Dolphins (Tursiops truncatus) Carrying Dead Calves

Pedrazzi

Giacomini

Pace

2022

Biology

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Epimeletic behavior toward dead calves has been frequently reported in cetaceans, mostly with females (presumed mothers) showing supportive behaviors such as carrying, lifting, or sinking, often assisted by “escort” individuals. However, information on acoustic production in such contexts is scarce. This report describes two observations of epimeletic behavior toward dead newborns in bottlenose dolphins and associated acoustic production. Data were collected at the Tiber River estuary (Rome, Mediterranean Sea, Italy) with one hydrophone for passive acoustic monitoring and two digital cameras. In both cases, an individual (presumed mother) acting as the main supporter and directly interacting with the carcass by lifting it (case 1) or sinking it (case 2) was observed. Another adult individual (escort) was present in both encounters showing standing-by and excitement behaviors (case 1) and supportive behavior (case 2). In both encounters, whistles, pulsed sounds, and bray-call elements were recorded. The consistent vocal activity observed likely conveyed context-specific information. A signature whistle in the first encounter was also recorded, likely emitted by the putative mother as a distress call. This report confirms the occurrence of epimeletic behavior in bottlenose dolphins and reports a preliminary description of the acoustic production when a dead calf is involved, providing additional information on this largely unknown topic.

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Section: Methodsmentioning

confidence: 99%

First Report of Epimeletic and Acoustic Behavior in Mediterranean Common Bottlenose Dolphins (Tursiops truncatus) Carrying Dead Calves

Pedrazzi

Giacomini

Pace

2022

Biology

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“…• Haphazard sampling, in which no explicit or structured selection strategy is employed by the auditor. The sampling is conducted without a specific reason for including or excluding journal entries [22].…”

Section: Attribute Sampling Techniquesmentioning

confidence: 99%

Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks

Schreyer

Sattarov

Gierbl

et al. 2020

Proceedings of the First ACM International Conference on AI in Finance

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The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.

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Section: Attribute Sampling Techniquesmentioning

confidence: 99%

Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks

Schreyer,

Sattarov,

Gierbl

et al. 2020

Preprint

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The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement âĂŹtrue and fair presentationâĂŹ. International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such entries, auditors regularly conduct a sample-based assessment referred to as 'audit sampling'. However, the task of audit sampling is often conducted early in the overall audit process. Often at a stage, in which an auditor might be unaware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the application of Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.

show abstract

Haphazard Sampling: Selection Biases and the Estimation Consequences of These Biases

Cited by 6 publications

References 3 publications

First Report of Epimeletic and Acoustic Behavior in Mediterranean Common Bottlenose Dolphins (Tursiops truncatus) Carrying Dead Calves

First Report of Epimeletic and Acoustic Behavior in Mediterranean Common Bottlenose Dolphins (Tursiops truncatus) Carrying Dead Calves

Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks

Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks

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