scholarly journals A comparison of three statistical methods for analysing extinction threat status

2013 ◽  
Vol 41 (1) ◽  
pp. 37-44 ◽  
Author(s):  
HEATHER R. TAFT ◽  
DEREK A. ROFF ◽  
ATTE KOMONEN ◽  
JANNE S. KOTIAHO
Keyword(s):  
Logistic Regression ◽  
Life History ◽  
Statistical Methods ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Statistical Models ◽  
Function Analysis ◽  
Regression Trees ◽  
Threat Status ◽  
Life History Characteristics

SUMMARYThe International Union for Conservation of Nature (IUCN) Red List provides a globally-recognized evaluation of the conservation status of species, with the aim of catalysing appropriate conservation action. However, in some parts of the world, species data may be lacking or insufficient to predict risk status. If species with shared ecological or life history characteristics also tend to share their risk of extinction, then ecological or life history characteristics may be used to predict which species may be at risk, although perhaps not yet classified as such by the IUCN. Statistical models may be a means to determine whether there are non-threatened or unclassified species that share the characteristics of threatened species, however there are no data on which model might be most appropriate or whether multiple models should be used. In this paper, three types of statistical models, namely regression trees, logistic regression and discriminant function analysis are compared using data on the ecological characteristics of Finnish lepidopterans (butterflies and moths). Overall, logistic regression performed slightly better than discriminant function analysis in predicting species status, and both outperformed regression trees. Uncertainty in species classification suggests that multiple analyses should be performed and particular attention devoted to those species for which the methods disagree. Such standard statistical methods may be a valuable additional tool in assessing the likely threat status of a species where there is a paucity of abundance data.

A Quantitative Method of Expressing Flex-Cracking Results

1940 ◽  
Vol 13 (3) ◽  
pp. 694-703
Author(s):  
R. G. Newton
Keyword(s):  
Statistical Methods ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Quantitative Method ◽  
Function Analysis ◽  
Good Reason ◽  
Test Method ◽  
Research Association ◽  
Test Results ◽  
Quantitative Results

Abstract In addition to providing a means of obtaining accurate quantitative results for individual flex-cracking tests, such as the De Mattia or Du Pont tests, the application of discriminant function analysis suggests a basis for standardizing the entire test method, so that objection to the test for specification purposes is thus concerned only with the reproducibility of the results, a problem which can now be investigated with greater confidence and ease. In connection with the accuracy of the method it may be contended that much depends on the subjective attitude of the tester who grades the samples with the photographs. When half-scores were not employed, the accuracy was limited mainly by the coarseness of the scale. This suggests that the grading is objective and, although no comparison, based upon statistical methods, has been made to determine the discrepancies between operators, no circumstance has occurred to suggest that important divergencies of opinion do exist. In addition, the analysis has been repeated on a further series of fifty samples, and the values found for the scoring constants were not substantially different from those obtained in this investigation. There is thus good reason to believe that this method of assessing the results will form a valuable means of investigating the causes of variation in flex-cracking test results and of recording the conclusions from the tests. The author wishes to thank the Board of Management of the Research Association of British Rubber Manufacturers for permission to publish the results given in this paper.

scholarly journals Pre-harvest forecast of pigeon-pea yield using discriminant function analysis of weather variables

MAUSAM ◽  
2021 ◽  
Vol 67 (3) ◽  
pp. 577-582
Author(s):  
R. R. YADAV ◽  
B. V. S. SISODIA ◽  
SUNIL KUMAR
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Statistical Models ◽  
Time Series Data ◽  
Function Analysis ◽  
Uttar Pradesh ◽  
Pigeon Pea ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Variables

In the present paper, an application of discriminant function analysis of weather variables (minimum & maximum temperature, Rainfall, Rainy days, Relative humidity 7 hr & 14 hr, Sunshine hour and Wind velocity )for developing suitable statistical models to forecast pigeon-pea yield in Faizabad district of Eastern Uttar Pradesh has been demonstrated. Time series data on pigeon-pea yield for 22 years (1990-91 to 2011-12) have been divided into three groups, viz., congenial, normal, and adverse based on de-trended yield distribution. Considering these groups as three populations, discriminant function analysis using weekly data on eight weather variables in different forms has been carried out. The sets of discriminant scores obtained from such analysis have been used as regressor variables along with time trend variable and pigeon-pea yield as regressand in development of statistical models. In all nine models have been developed. The forecast yield of pigeon-pea have been obtained from these models for the year 2009-10, 2010-11 and 2011-12, which were not included in the development of the models. The model 4 and 9 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of pigeon-pea yield about two and half months before the crop harvest.

scholarly journals Getting Rid of Dichotomous Sex Estimations: Why Logistic Regression Should be Preferred Over Discriminant Function Analysis

2020 ◽  
Vol 65 (5) ◽  
pp. 1685-1691
Author(s):  
Bjørn Peare Bartholdy ◽  
Elena Sandoval ◽  
Menno L. P. Hoogland ◽  
Sarah A. Schrader
Keyword(s):  
Logistic Regression ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis

scholarly journals Use of ordinal logistic regression in crop yield forecasting

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 913-918
Author(s):  
VANDITA KUMARI ◽  
RANJANA AGRAWAL ◽  
AMRENDER KUMAR
Keyword(s):  
Logistic Regression ◽  
Crop Yield ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Ordinal Logistic Regression ◽  
Percentage Error ◽  
Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.  

Classification of rural households due to directions and types of outgoings

2017 ◽  
Vol 62 (8) ◽  
pp. 53-73
Author(s):  
Marlena Piekut
Keyword(s):  
Statistical Methods ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Rural Households ◽  
Analysis Data ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Per Capita

The aim of the study is to isolate groups of rural households with similar outgoings and to describe them by socio-demographic and economic characteristics. It was carried out using multivariate statistical methods such as k-means cluster and discriminant function analysis. Data from the CSO survey of household budgets for the years 2004 and 2012 were used for the research purpose. The research resulted in the division of rural households into four groups considering the outgoings, where one group covered more than 2/3 of the households. Variables which discriminated the membership of rural households to certain groups to the largest extent were the number of people in the household and disposable income per capita.

Predicting Public Procurement Irregularity: An Application of Neural Networks

2018 ◽  
Vol 15 (1) ◽  
pp. 141-154 ◽  
Author(s):  
Ting Sun ◽  
Leonardo J. Sales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Prediction Models ◽  
Function Analysis ◽  
Public Procurement ◽  
Predictive Performance ◽  
Z Scores

ABSTRACT Using the data describing the characteristics of contractors provided by the Comptroller General of the Union, Brazil (CGU), this paper mainly implements two artificial neural networks, traditional neural network (TNN) and deep neural network (DNN), to develop prediction models of public procurement irregularities designed for the initial screening of contractors. This is the first application of DNN in the context of government auditing. To examine the effectiveness of DNN, the authors compare its predictive performance to TNN and two other algorithms (logistic regression and discriminant function analysis) and find that DNN significantly outperforms TNN and other algorithms in terms of accuracy, precision, F-scores, AUC, and other metrics, as suggested by the high Z-scores of the Z-tests. Although TNN has a higher recall than DNN, the difference of recall between TNN and DNN is insignificant. Logistic regression and discriminant function analysis achieve the highest recall scores, but their Z-scores are much lower than those of other metrics. Therefore, DNN generally performs more accurately than other approaches and meets the requirement of the CGU for an early alarm system.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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