Dating Lower Turning Points of Business Cycles – A Multivariate Linear Discriminant Analysis for Germany 1974 to 2009

2019 ◽  
pp. 173-185
Author(s):  
Ullrich Heilemann ◽  
Heinz Josef Münch
Keyword(s):  
Discriminant Analysis ◽  
Business Cycles ◽  
Linear Discriminant Analysis ◽  
Turning Points ◽  
Linear Discriminant

Zur Evolution des deutschen Konjunkturzyklus 1958-2004 / On the Evolution of German Business Cycles 1958-2004

2008 ◽  
Vol 228 (1) ◽  
Author(s):  
Ullrich Heilemann ◽  
Roland Schuhr
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Business Cycles ◽  
Business Cycle ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Classification Methods ◽  
The West ◽  
Linear Discriminant ◽  
Macroeconomic Variables

SummaryThis paper examines changes of the (West) German business cycle from 1958 to 2004. It starts with a multivariate linear discriminant analysis (LDA) based decomposition of the cycle into 4 phases (upswing, upper turning point, downswing, lower turning point). After examining inter-cyclical changes of the cycle, i.e. changes of the weights of the 12 macroeconomic variables employed for classification, the question of intra-cyclical changes is addressed. This is done by using DLDA, a new dynamic variant of LDA which exploits the time series character of the data used to analyse changes of the multivariate structure of the cycle. The DLDA results exemplify that the transition from one to the next phase is much smoother and more continuous than might be expected. Within the sample examined these movements vary as well as the weights attributed to the classifying variables. In a methodological perspective DLDA turns out to be a promising broadening of classification methods.

Classification of West German Business Cycles 1955-1994 / Klassifikation westdeutscher Konjunkturzyklen 1955 - 1994

1999 ◽  
Vol 219 (5-6) ◽  
Author(s):  
Ullrich Heilemann ◽  
Heinz Josef Münch
Keyword(s):  
Discriminant Analysis ◽  
Business Cycles ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Explanatory Power ◽  
Linear Discriminant ◽  
Fourth Cycle ◽  
Selection Of

SummaryThis paper applies multivariate linear discriminant analysis to classify West German business cycles from 1955 to 1994 into a four phase scheme (upswing, downswing, and upper/lower turning point phases). It describes the scheme as well as the selection of the classifying variables, and presents classification results for various sample periods confirming the four phase scheme. Special attention is given to changes of the explanatory power of the variables and its implication for changes of cycle patterns. While for the 1963/94 period the results display all in all much stability, the 1955/62 period does not fit into this pattern, indicating a change of regime which might even reach into the fourth cycle (1963-1967).

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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