scholarly journals Investigation on Quantitative Structure-Activity Relationships of 1,3,4-Oxadiazole Derivatives as Potential Telomerase Inhibitors

2020 ◽  
Vol 17 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Marco Tutone ◽  
Beatrice Pecoraro ◽  
Anna M. Almerico
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Chemical Information ◽  
Telomerase Inhibition ◽  
Telomerase Inhibitors ◽  
Potential Marker ◽  
Ic50 Values ◽  
Oxadiazole Derivatives

Background:Telomerase, a reverse transcriptase, maintains telomere and chromosomes integrity of dividing cells, while it is inactivated in most somatic cells. In tumor cells, telomerase is highly activated, and works in order to maintain the length of telomeres causing immortality, hence it could be considered as a potential marker to tumorigenesis.A series of 1,3,4-oxadiazole derivatives showed significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition.Methods:This series of 24 N-benzylidene-2-((5-(pyridine-4-yl)-1,3,4-oxadiazol-2yl)thio)acetohydrazide derivatives as telomerase inhibitors has been considered to carry out QSAR studies. The endpoint to build QSAR models is determined by the IC50 values for telomerase inhibition, i.e., the concentration (μM) of inhibitor that produces 50% inhibition. These values were converted to pIC50 (- log IC50) values. We used the most common and transparent method, where models are described by clearly expressed mathematical equations: Multiple Linear Regression (MLR) by Ordinary Least Squares (OLS).Results:Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds.Conclusion:The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.

scholarly journals PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah)

2019 ◽  
Vol 8 (1) ◽  
pp. 81-92
Author(s):  
Dhea Kurnia Mubyarjati ◽  
Abdul Hoyyi ◽  
Hasbi Yasin
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Predictor Variables ◽  
Fish Production ◽  
Significance Level ◽  
Central Java ◽  
Approximate Estimation

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

2019 ◽  
Vol 13 (1) ◽  
pp. 37-58
Author(s):  
Ilma Yuni Rosita ◽  
Lilis Imamah Ichdayati ◽  
Rizki Adi Puspita Sari
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
International Market ◽  
Ordinary Least Squares ◽  
Cocoa Beans ◽  
Significance Level ◽  
The Real ◽  
Analyze Time Series

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

2019 ◽  
Vol 11 (2) ◽  
pp. 161-182
Author(s):  
Ilma Yuni Rosita ◽  
Lilis Imamah Ichdayati ◽  
Rizki Adi Puspita Sari
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
International Market ◽  
Ordinary Least Squares ◽  
Cocoa Beans ◽  
Significance Level ◽  
The Real ◽  
Analyze Time Series

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

THE PERFORMANCE OF LEVERAGE BASED NEAR NEIGHBOUR-ROBUST WEIGHT LEAST SQUARES IN MULTIPLE LINEAR REGRESSION IN THE PRESENCE OF HETEROSCEDASTIC ERRORS AND OUTLIER

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Khoo Li Peng ◽  
Robiah Adnan ◽  
Maizah Hura Ahmad
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Standard Error ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Standard Errors ◽  
Data Sets ◽  
Near Neighbour ◽  
Heteroscedastic Errors

In this study, Leverage Based Near Neighbour–Robust Weighted Least Squares (LBNN-RWLS) method is proposed in order to estimate the standard error accurately in the presence of heteroscedastic errors and outliers in multiple linear regression. The data sets used in this study are simulated through monte carlo simulation. The data sets contain heteroscedastic errors and different percentages of outliers with different sample sizes.  The study discovered that LBNN-RWLS is able to produce smaller standard errors compared to Ordinary Least Squares (OLS), Least Trimmed of Squares (LTS) and Weighted Least Squares (WLS). This shows that LBNN-RWLS can estimate the standard error accurately even when heteroscedastic errors and outliers are present in the data sets.

scholarly journals Determinant of Cocoa Export in Indonesia

2018 ◽  
Vol 7 (3) ◽  
pp. 286-293
Author(s):  
Medha Wardhany ◽  
Fauzul Adzim
Keyword(s):  
International Trade ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Analytical Method ◽  
Ordinary Least Squares ◽  
Cocoa Beans ◽  
Export Volume ◽  
Us Dollar

International Trade is one of the activities that plays an important role for the economy. Indonesia is one of the countries whose depends on exports. One of the agricultural commodities that become the leading commodity is cocoa. Although it is a main flag export commodity, cocoa farming still has many challenges. The export volume of cocoa beans in the period 1987-2016 increase slightly, but in the last six years the export tend to decrease. The purpose of this study is to analyze the factors that affect the export of cocoa beans. The analytical method used is Multiple Linear Regression with the ordinary least squares rank method (OLS). The results showed that the variables of production have a positive and significant effect with coefficient value of 0.642607. Domestic cocoa price does not affect the export volume of cocoa beans. The international cocoa price variable has a negative and significant effect on export volume of Indonesian cocoa beans with coefficient value of -7,073793. The rupiah exchange rate variable to US Dollar has a positive and significant effect on the export volume of Indonesian cocoa beans with coefficient value of 15.22362. While simultaneously, production variables, domestic cocoa prices, international cocoa prices, and Rupiah exchange rate against US Dollar together affect the export volume of Indonesian cocoa beans.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Mortalité du sapin baumier défolié par la tordeuse des bourgeons de l'épinette dans la région de la Gatineau au Québec

1982 ◽  
Vol 58 (5) ◽  
pp. 213-219 ◽  
Author(s):  
Jean Beaulieu ◽  
Yvan J. Hardy
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Random Number ◽  
Least Squares Method ◽  
Random Number Generator ◽  
Balsam Fir ◽  
Number Generator ◽  
Profit Analysis ◽  
Significant Difference

This paper presents a method of analysis which differentiates between spruce budworm caused mortality and regular mortality on balsam fir in the Gatineau region in Quebec. A first attempt was made using multiple linear regression and a uniform random number generator. In order to overcome the bias inherent to the least squares method when dealing with a binary (0,1) dependent variable, a profit analysis was also conducted. In this case, the parameters and their variance were estimated using likehood method. These two approaches proved to be equivalent when percent budworm caused mortality was compared within the 1958 to 1979 period covered by the data at hand, while the outbreak lasted from 1968 to 1975.In 1979, approximately 55% of the stems had been killed by the budworm, accounting for 53% of the volume. Maple-yellow birch associations were more affected than fir associations although no significant difference was found. Fir mortality was delayed by aerial spraying of insecticides but this advantage disappeared as soon as the spray operations came to an end.

Robust Weighted Least Squares Estimation of Regression Parameter in the Presence of Outliers and Heteroscedastic Errors

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Bello Abdulkadir Rasheed ◽  
Robiah Adnan ◽  
Seyed Ehsan Saffari ◽  
Kafi Dano Pati
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Model Parameters ◽  
Constant Variance ◽  
Regression Parameters ◽  
M Estimation

In a linear regression model, the ordinary least squares (OLS) method is considered the best method to estimate the regression parameters if the assumptions are met. However, if the data does not satisfy the underlying assumptions, the results will be misleading. The violation for the assumption of constant variance in the least squares regression is caused by the presence of outliers and heteroscedasticity in the data. This assumption of constant variance (homoscedasticity) is very important in linear regression in which the least squares estimators enjoy the property of minimum variance. Therefor e robust regression method is required to handle the problem of outlier in the data. However, this research will use the weighted least square techniques to estimate the parameter of regression coefficients when the assumption of error variance is violated in the data. Estimation of WLS is the same as carrying out the OLS in a transformed variables procedure. The WLS can easily be affected by outliers. To remedy this, We have suggested a strong technique for the estimation of regression parameters in the existence of heteroscedasticity and outliers. Here we apply the robust regression of M-estimation using iterative reweighted least squares (IRWLS) of Huber and Tukey Bisquare function and resistance regression estimator of least trimmed squares to estimating the model parameters of state-wide crime of united states in 1993. The outcomes from the study indicate the estimators obtained from the M-estimation techniques and the least trimmed method are more effective compared with those obtained from the OLS.

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