Predicting biological parameters of estuarine benthic communities using models based on environmental data

This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness) of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth). Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA), and the second based on Multiple Linear Regression (MLR). Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.

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Predictors of Financial Distress and Bankruptcy Model Construction

THE INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION ◽

10.18775/ijmsba.1849-5664-5419.2014.26.1003 ◽

2014 ◽

Vol 2 (6) ◽

pp. 34-42 ◽

Cited By ~ 2

Author(s):

Dana Kubíčková Kubíčková ◽

Vladimír Nulíček

Keyword(s):

Discriminant Analysis ◽

Predictive Models ◽

Financial Distress ◽

Model Construction ◽

Multiple Discriminant Analysis ◽

Financial Situation ◽

Transition Economics ◽

New Model ◽

Special Regard ◽

Multivariate Discriminant

The aim of this paper is to prepare the bankruptcy model construction. In the first part, multivariate discriminant analysis and its possibilities in deriving predictive models are characterized. The second part defines the possible indicators/predictors of financial distress of companies, which could be included in the new bankruptcy model. The model itself compares different views of factors that affect the company’s financial situation and contrasts the indicators that were constructed in the model in previous works (with special regard to the models in the transition economics). The result is the collection of 39 indicators to be verified in the next stage of the research project employing the multiple discriminant analysis methods to specify which of them to be included in the new model.

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Monthly and Seasonal Rainfall Forecasting in Southern Brazil Using Multiple Discriminant Analysis

Weather and Forecasting ◽

10.1175/waf-d-15-0155.1 ◽

2016 ◽

Vol 31 (6) ◽

pp. 1947-1960 ◽

Cited By ~ 4

Author(s):

Denilson Ribeiro Viana ◽

Clóvis Angeli Sansigolo

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Principal Component ◽

Skill Score ◽

Seasonal Rainfall ◽

Rainfall Time Series ◽

Southern Brazil ◽

Multiple Discriminant Analysis ◽

Rainfall Forecasting ◽

Global Precipitation Climatology Centre

Abstract A multiple discriminant analysis was employed to forecast monthly and seasonal rainfall in southern Brazil. The methodology used includes six steps: data acquisition, preprocessing, feature extraction, feature selection, classification, and evaluation. The predictors (atmospheric, surface, and oceanic variables) and predictand (rainfall) were obtained from the Twentieth Century Reanalysis (version 2), as well as from the HadISST1 (Met Office Hadley Centre) and Global Precipitation Climatology Centre (GPCC) databases. The definition of key regions (feature extraction step) was performed using spatial principal component analysis. In the selection step, the rainfall time series were allocated into terciles, which were related to the predictors via multiple discriminating analyses. The results revealed that ⅓ of the predictors are associated with atmospheric pressure and also emphasized the role of atmospheric circulation over the Antarctic region and its surroundings. Surface variables (albedo and soil moisture) were also of great importance in the forecasting. The average skill score (gain over climatology) was 29%. It is concluded that the proposed model is a reliable alternative for use in forecasting monthly and seasonal rainfall over southern Brazil.

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Comparison of Examinees’ and Doctors’ Judgement of Health Status Using Data in an AMHTS

Methods of Information in Medicine ◽

10.1055/s-0038-1635319 ◽

1984 ◽

Vol 23 (01) ◽

pp. 15-22

Author(s):

Y. Sekita ◽

T. Ohta ◽

M. Inoue ◽

H. Takeda

Keyword(s):

Health Status ◽

Discriminant Analysis ◽

Explanatory Power ◽

Laboratory Data ◽

Subjective Symptom ◽

Health Conditions ◽

Testing System ◽

Multiple Discriminant Analysis ◽

Canonical Axis ◽

Using Data

SummaryJudgements of examinees’ health status by doctors and by the examinees themselves are compared applying multiple discriminant analysis. The doctors’ judgements of the examinees’ health status are studied comparatively using laboratory data and the examinees’ subjective symptom data.This data was obtained in an Automated Multiphasic Health Testing System. We discuss the health conditions which are significant for the judgement of doctors about the examinees. The results show that the explanatory power, when using subjective symptom data, is fair in the case of the doctors’ judgement. We found common variables, such as nervousness, lack of perseverance etc., which form the first canonical axis.

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The validity of diagnostic systems for common mental disorders: a comparison between the ID-CATEGO and the DSM-III systems

Psychological Medicine ◽

10.1017/s0033291700013386 ◽

1990 ◽

Vol 20 (1) ◽

pp. 209-218 ◽

Cited By ~ 11

Author(s):

David Grayson ◽

Keith Bridges ◽

Diane Cook ◽

David Goldberg

Keyword(s):

Discriminant Analysis ◽

Depressive Disorders ◽

Common Mental Disorders ◽

Latent Trait ◽

Mental Illnesses ◽

Anxiety And Depression ◽

Multiple Discriminant Analysis ◽

Diagnostic Systems ◽

Scale Scores ◽

Initial Scale

SYNOPSISIt is argued that latent trait analysis provides a way of examining the construct validity of diagnostic concepts which are used to categorize common mental illnesses. The present study adds two additional aspects of validity using multiple discriminant analysis applied to two widely used taxonomic systems. Scales of anxiety and depression derived from previous latent trait analyses are applied to individuals reaching criteria for ‘caseness’ on the ID-CATEGO system and the DSM-III system, both at initial diagnosis and six months later. The first multiple discriminant analysis is carried out on the initial scale scores, and the results are interpreted in terms of concurrent validity. The second analysis uses improvement scores on the two scales and relates to predictive validity. It is argued that the ID-CATEGO system provides a better classification for common mental illnesses than the DSM-III system, since it allows a better discrimination to be made between anxiety and depressive disorders.

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