Tests of Hypotheses About the Parameters of the Logistic Function

This chapter presents an introductory overview of concepts that underscore the general framework of item response theory. “Item response theory” is a broad umbrella term used to describe a family of mathematical measurement models that consider observed test scores to be a function of latent, unobservable constructs. Most musical constructs cannot be directly measured and are therefore unobservable. Musical constructs can therefore only be inferred based on secondary, observable behaviors. Item response theory uses observable behaviors as probabilistic distributions of responses as a logistic function of person and item parameters in order to define latent constructs. This chapter describes philosophical, theoretical, and applied perspectives of item response theory in the context of measuring musical behaviors.

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Effects of habitat edges on vegetation structure and the vulnerable golden-brown mouse lemur (Microcebus ravelobensis) in northwestern Madagascar

BMC Ecology ◽

10.1186/s12898-020-00337-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Bertrand Andriatsitohaina ◽

Daniel Romero-Mujalli ◽

Malcolm S. Ramsay ◽

Frederik Kiene ◽

Solofonirina Rasoloharijaona ◽

...

Keyword(s):

Body Mass ◽

Edge Effects ◽

Vegetation Structure ◽

Species Abundance ◽

Tree Height ◽

Logistic Function ◽

Study Region ◽

Large Trees ◽

Significant Difference ◽

Vegetation Parameters

Abstract Background Edge effects can influence species composition and community structure as a result of changes in microenvironment and edaphic variables. We investigated effects of habitat edges on vegetation structure, abundance and body mass of one vulnerable Microcebus species in northwestern Madagascar. We trapped mouse lemurs along four 1000-m transects (total of 2424 trap nights) that ran perpendicular to the forest edge. We installed 16 pairs of 20 m2 vegetation plots along each transect and measured nine vegetation parameters. To determine the responses of the vegetation and animals to an increasing distance to the edge, we tested the fit of four alternative mathematical functions (linear, power, logistic and unimodal) to the data and derived the depth of edge influence (DEI) for all parameters. Results Logistic and unimodal functions best explained edge responses of vegetation parameters, and the logistic function performed best for abundance and body mass of M. ravelobensis. The DEI varied between 50 m (no. of seedlings, no. of liana, dbh of large trees [dbh ≥ 10 cm]) and 460 m (tree height of large trees) for the vegetation parameters, whereas it was 340 m for M. ravelobensis abundance and 390 m for body mass, corresponding best to the DEI of small tree [dbh < 10 cm] density (360 m). Small trees were significantly taller and the density of seedlings was higher in the interior than in the edge habitat. However, there was no significant difference in M. ravelobensis abundance and body mass between interior and edge habitats, suggesting that M. ravelobensis did not show a strong edge response in the study region. Finally, regression analyses revealed three negative (species abundance and three vegetation parameters) and two positive relationships (body mass and two vegetation parameters), suggesting an impact of vegetation structure on M. ravelobensis which may be partly independent of edge effects. Conclusions A comparison of our results with previous findings reveals that edge effects are variable in space in a small nocturnal primate from Madagascar. Such an ecological plasticity could be extremely relevant for mitigating species responses to habitat loss and anthropogenic disturbances.

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Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Energies ◽

10.3390/en14041091 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1091

Author(s):

Liulei Bao ◽

Guangcheng Zhang ◽

Xinli Hu ◽

Shuangshuang Wu ◽

Xiangdong Liu

Keyword(s):

Direct Method ◽

Logistic Function ◽

New Method ◽

Threshold Function ◽

Critical Threshold ◽

Time Curve ◽

Acceleration Rate ◽

Deformation Stages ◽

Distance Weighting ◽

Landslide Deformation

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

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Tests of Hypotheses for the Weighted Binomial Distribution

Biometrics ◽

10.2307/2532085 ◽

1990 ◽

Vol 46 (3) ◽

pp. 645 ◽

Cited By ~ 6

Author(s):

S. Kocherlakota ◽

K. Kocherlakota

Keyword(s):

Binomial Distribution ◽

Tests Of Hypotheses

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Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽

10.3390/info12030109 ◽

2021 ◽

Vol 12 (3) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Iman Rahimi ◽

Amir H. Gandomi ◽

Panagiotis G. Asteris ◽

Fang Chen

Keyword(s):

Machine Learning ◽

Logistic Function ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Model Parameters ◽

The Novel ◽

Chinese City ◽

Limited Memory ◽

Increasing Trend ◽

Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

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Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽

10.3390/w13050705 ◽

2021 ◽

Vol 13 (5) ◽

pp. 705

Author(s):

Josué Trejo-Alonso ◽

Carlos Fuentes ◽

Carlos Chávez ◽

Antonio Quevedo ◽

Alfonso Gutierrez-Lopez ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Hydraulic Conductivity ◽

Input Data ◽

Logistic Function ◽

Saturated Hydraulic Conductivity ◽

Irrigation District ◽

Pedotransfer Functions ◽

Back Propagation Algorithm ◽

Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

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New Zealand translocations: theory and practice

Pacific Conservation Biology ◽

10.1071/pc950039 ◽

1995 ◽

Vol 2 (1) ◽

pp. 39 ◽

Cited By ~ 65

Author(s):

Doug P. Armstong ◽

Ian G. McLean

Keyword(s):

Population Dynamics ◽

New Zealand ◽

Experimental Tests ◽

Theory And Practice ◽

Habitat Requirements ◽

Metapopulation Dynamics ◽

Behavioural Adaptation ◽

New Locations ◽

History Of ◽

Tests Of Hypotheses

One of the most common tools in New Zealand conservation is to translocate species to new locations. There have now been over 400 translocations done for conservation reasons, mainly involving terrestrial birds. Most translocations have been done strictly as management exercises, with little or no reference to theory. Nevertheless, translocations always involve some underlying theory, given that people must inevitably choose among a range of potential translocation strategies. We review theory relevant to translocations in the following areas: habitat requirements, susceptibility to predation, behavioural adaptation, population dynamics, genetics, metapopulation dynamics, and community ecology. For each area we review and evaluate the models that seem to underpin translocation strategies used in New Zealand. We report experiments testing some of these models, but note that theory underlying translocation strategies is largely untested despite a long history of translocations. We conclude by suggesting key areas for research, both theoretical and empirical. We particularly recommend that translocations be designed as experimental tests of hypotheses whenever possible.

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A Note on the Estimation of the Parameters of the Logistic Function, Using the Minimum Logit χ 2 Method

Biometrika ◽

10.2307/2333488 ◽

1962 ◽

Vol 49 (1/2) ◽

pp. 250 ◽

Cited By ~ 1

Author(s):

Shirley E. Hitchcock

Keyword(s):

Logistic Function

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