A simple method for classifying data in relative growth analysis of crustaceans

Crustaceana ◽  
2019 ◽  
Vol 92 (11-12) ◽  
pp. 1435-1443
Author(s):  
René Zambrano
Keyword(s):  
Linear Regression ◽  
Conventional Method ◽  
Regression Line ◽  
Real Data ◽  
Simple Method ◽  
Relative Growth ◽  
Linear Discriminant ◽  
Residual Difference ◽  
Wide Range ◽  
Total Data

Abstract Relative growth in crustaceans is a topic of quite high importance that allows, among other things, to estimate their size at the onset of morphometric sexual maturity. In this process, it is usual to start from a transition point and then generate regressions that reflect phases of the sexual development of the individuals (i.e., mature and immature). There are several statistical methods that allow data to be classified into subsets, but according to the specific growth pattern at issue, those methods may or may not be usable. This paper proposes a simple method to classify data into two subsets, viz., when they are overlapping over a wide range of sizes. The actual procedure consists of a linear regression that divides the data. Subsequently, a linear regression is applied to the groups generated by adjusting the parameters through maximum log-likelihood. The observed values will be classified according to the smallest residual difference generated with each regression line. The proposed method was tested by separating the sexes of Goyazana castelnaui, using real data. The efficiency of the method was analysed based on the percentage value between the number of total data and the number of correctly classified data. Additionally, the k-mean cluster was used as a conventional method, the results of which were reclassified by a linear discriminant analysis. The efficiency of the proposed method was >80% while that of the conventional method was >60%. The values misclassified by the proposed method were mixed with those of the opposite sex, so it was expected to fail in those cases. The proposed method is a simple alternative that can serve as a basis for subsequent morphometric analysis, especially for acquiring an initial insight in the structure of a dataset collected for a study of relative growth.

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

Determination of core zone of marine sanctuary in Bahoi Village, North Minahasa Regency

2013 ◽  
pp. 10
Author(s):  
Sonny Tasidjawa ◽  
Stephanus V Mandagi ◽  
Ridwan Lasabuda
Keyword(s):  
Conventional Method ◽  
Local Community ◽  
Marine Protected Area ◽  
Final Decision ◽  
Marine Biota ◽  
Small Community ◽  
Community Based ◽  
Simple Method ◽  
Core Zone ◽  
The Core

Bahoi village is located in West Likupang District of North Minahasa Regency. It is one of the villages that is included in the conservation network of North Sulawesi Province. A marine sanctuary has been established in this village in 2003 and it has been managed by local community, known as community-based marine sanctuary management, since then, this sanctuary has been in operation. As a small community-based marine protected area with lots of users, it requires an appropriate method to determine the Core Zone that allows an effective preservation of the marine biota. This is the driving factor of this study.  The purpose of this study is to examine the processes and output of determining the core zone of a Marine Sanctuary using a conventional method and Marxan Method. The conventional method is a simple method in determining a core zone such as using manta tow technique. While Marxan, it only requires input of data such as spatial and figures to generate information for determining the core zone. After comparing the processes of these two methods in the study site, it was found that Marxan method was more effective and more accurate with lower costs than the conventional one. In addition, the final decision of the core zone depended on the outcome of the village meetings when the conventional method was applied. This long process could be avoided when Marxan method was used. Therefore, it is highly recommended to use Marxan in determining core zones© Desa Bahoi terletak di Kecamatan Likupang Barat Kabupaten Minahasa Utara. Desa ini merupakan salah satu desa yang masuk dalam jejaringan kawasan konservasi di Provinsi Sulawesi Utara. Sebuah Daerah Perlindungan Laut telah didirikan di desa ini pada tahun 2003 dan dikelolah oleh masyarakat setempat, yang dikenal sebagai pengelolaan Daerah Perlindungan Laut Berbasis Masyarakat, sejak saat itu Daerah Perlindungan Laut ini telah beroperasi. Sebagai Daerah Perlindungan Laut Berbasis Masyarakat yang kecil namun memiliki banyak pengguna, diperlukan metode tepat yang akan menentukan Zona Inti yang memungkinkan pelestarian biota laut menjadi sangat efektif. Ini adalah faktor pendorong dari penelitian. Selanjutnya, tujuan dari penelitian ini adalah untuk mengkaji proses dan hasil penentuan zona inti Daerah Perlindungan Laut dengan menggunakan metode konvensional seperti survei manta tow dan marxan. Metode konvensional adalah metode sederhana dalam menentukan zona inti seperti teknik manta tow. Sedangkan marxan, hanya perlu memasukan data seperti spasial dan angka untuk menghasilkan informasi penentuan zona inti. Setelah membandingkan proses dari dua metode di lokasi penelitian, ditemukan bahwa metode marxan jauh lebih baik dari pada metode konvensional, karena lebih efektif, lebih akurat dengan biaya yang lebih rendah. Selain itu, keputusan akhir dari zona inti tergantung pada hasil rapat desa ketika metode konvensional diterapkan, proses panjang ini dapat dihindari jika metode marxan digunakan©

scholarly journals Developing Equations for Converting Digestible Energy to Metabolizable Energy for Korean Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1696
Author(s):  
Ridha Ibidhi ◽  
Rajaraman Bharanidharan ◽  
Jong-Geun Kim ◽  
Woo-Hyeong Hong ◽  
In-Sik Nam ◽  
...  
Keyword(s):  
Linear Regression ◽  
Beef Cattle ◽  
Strong Relationship ◽  
Dietary Factors ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Male And Female ◽  
Digestible Energy ◽  
Wide Range

This study was performed to update and generate prediction equations for converting digestible energy (DE) to metabolizable energy (ME) for Korean Hanwoo beef cattle, taking into consideration the gender (male and female) and body weights (BW above and below 350 kg) of the animals. The data consisted of 141 measurements from respiratory chambers with a wide range of diets and energy intake levels. A simple linear regression of the overall unadjusted data suggested a strong relationship between the DE and ME (Mcal/kg DM): ME = 0.8722 × DE + 0.0016 (coefficient of determination (R2) = 0.946, root mean square error (RMSE) = 0.107, p < 0.001 for intercept and slope). Mixed-model regression analyses to adjust for the effects of the experiment from which the data were obtained similarly showed a strong linear relationship between the DE and ME (Mcal/kg of DM): ME = 0.9215 × DE − 0.1434 (R2 = 0.999, RMSE = 0.004, p < 0.001 for the intercept and slope). The DE was strongly related to the ME for both genders: ME = 0.8621 × DE + 0.0808 (R2 = 0.9600, RMSE = 0.083, p < 0.001 for the intercept and slope) and ME = 0.7785 × DE + 0.1546 (R2 = 0.971, RMSE = 0.070, p < 0.001 for the intercept and slope) for male and female Hanwoo cattle, respectively. By BW, the simple linear regression similarly showed a strong relationship between the DE and ME for Hanwoo above and below 350 kg BW: ME = 0.9833 × DE − 0.2760 (R2 = 0.991, RMSE = 0.055, p < 0.001 for the intercept and slope) and ME = 0.72975 × DE + 0.38744 (R2 = 0.913, RMSE = 0.100, p < 0.001 for the intercept and slope), respectively. A multiple regression using the DE and dietary factors as independent variables did not improve the accuracy of the ME prediction (ME = 1.149 × DE − 0.045 × crude protein + 0.011 × neutral detergent fibre − 0.027 × acid detergent fibre + 0.683).

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

scholarly journals Sparse Functional Linear Discriminant Analysis

Biometrika ◽  
2021 ◽  
Author(s):  
Juhyun Park ◽  
Jeongyoun Ahn ◽  
Yongho Jeon
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real Data ◽  
New Approach ◽  
Linear Discriminant ◽  
Finite Samples ◽  
Regularization Problem ◽  
Bayes Error ◽  
Functional Setting ◽  
Regularized Solution

Abstract Functional linear discriminant analysis offers a simple yet efficient method for classification, with the possibility of achieving a perfect classification. Several methods are proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is a growing interest in interpretability of the analysis, which favors a simple and sparse solution. In this work, we propose a new approach that incorporates a type of sparsity that identifies nonzero sub-domains in the functional setting, offering a solution that is easier to interpret without compromising performance. With the need to embed additional constraints in the solution, we reformulate the functional linear discriminant analysis as a regularization problem with an appropriate penalty. Inspired by the success of ℓ1-type regularization at inducing zero coefficients for scalar variables, we develop a new regularization method for functional linear discriminant analysis that incorporates an L1-type penalty, ∫ |f|, to induce zero regions. We demonstrate that our formulation has a well-defined solution that contains zero regions, achieving a functional sparsity in the sense of domain selection. In addition, the misclassification probability of the regularized solution is shown to converge to the Bayes error if the data are Gaussian. Our method does not presume that the underlying function has zero regions in the domain, but produces a sparse estimator that consistently estimates the true function whether or not the latter is sparse. Numerical comparisons with existing methods demonstrate this property in finite samples with both simulated and real data examples.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals Credit concession through credit scoring: Analysis and application proposal

2017 ◽  
Vol 13 (1) ◽  
pp. 51 ◽  
Author(s):  
Oriol Amat ◽  
Raffaele Manini ◽  
Marcos Antón Renart
Keyword(s):  
Financial Institutions ◽  
Design Methodology ◽  
Credit Scoring ◽  
Real Data ◽  
Statistical Techniques ◽  
Probability Of Default ◽  
Relative Importance ◽  
Linear Discriminant ◽  
Scoring Model ◽  
Credit Scoring Model

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations:  This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications:  Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.

scholarly journals Automatic and Online Detection of Rotor Fault State

2018 ◽  
Vol 7 (1) ◽  
pp. 43 ◽  
Author(s):  
Ali Ouanas ◽  
Ammar Medoued ◽  
Salim Haddad ◽  
Mourad Mordjaoui ◽  
D. Sayad
Keyword(s):  
Induction Motor ◽  
Dimensional Space ◽  
Operating Conditions ◽  
Online Detection ◽  
K Nearest Neighbor ◽  
Simple Method ◽  
Important Place ◽  
Motor Test ◽  
Wide Range ◽  
Fault State

In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available onlineHow to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.http://dx.doi.org/10.14710/ijred.7.1.43-52

scholarly journals Reliability of Nitrogen Content (%N) and Carbon:Nitrogen Atomic Ratios (C:N) as Indicators of Collagen Preservation Suitable for Radiocarbon Dating

Radiocarbon ◽  
2012 ◽  
Vol 54 (3-4) ◽  
pp. 879-886 ◽  
Author(s):  
Fiona Brock ◽  
Rachel Wood ◽  
Thomas F G Higham ◽  
Peter Ditchfield ◽  
Alex Bayliss ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Radiocarbon Dating ◽  
Linear Regression Analysis ◽  
Atomic Weight ◽  
Animal Bones ◽  
Bone Powder ◽  
Wide Range ◽  
Pass Through ◽  
Percent Nitrogen

A recent study into prescreening techniques to identify bones suitable for radiocarbon dating from sites known for poor or variable preservation (Brock et al. 2007, 2010a) found that the percent nitrogen (%N) content of whole bone powder was the most reliable indicator of collagen preservation. Measurement of %N is rapid, requires little preparation or material, and is relatively cheap. The technique reduces the risk of needlessly sampling valuable archaeological objects, as well as saving time and money on their unsuccessful pretreatment prior to dating. This method of prescreening is now regularly used at the Oxford Radiocarbon Accelerator Unit (ORAU). In the original study, linear regression analysis of data from 100 bones from 12 Holocene sites across southern England showed that when 0.76% N was chosen as a threshold, 84% of bones were successfully identified as containing sufficient (i.e. >1%) or insufficient (i.e. <1%) collagen for dating. However, it has been observed that for older, Pleistocene bones the failure rate may be higher, possibly due to the presence of more degraded, short-chain proteins that pass through the ultrafilters used in pretreatment, resulting in lower yields. Here, we present linear regression analysis of data from nearly 600 human and animal bones, antlers, and teeth, from a wide range of contexts and ages, to determine whether the 0.76% threshold identified in the previous study is still applicable. The potential of carbon:nitrogen atomic weight ratios (C:N) of whole bone to predict collagen preservation is also discussed.

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