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Research Article

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation

[version 1; peer review: 3 approved with reservations]
PUBLISHED 08 Jul 2021
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Abstract

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity.
Methods: A simulation study and a real-life study were carried out and the performance of the new estimator was compared with some of the existing estimators.
Results: The simulation result showed the new estimator performed more efficiently than the MLE, Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL (PKL) estimators. The real-life application also agreed with the simulation result.
Conclusions: In general, the new estimator performed more efficiently than the MLE, PRE, PLE and the PKL when multicollinearity was present.

Keywords

Linear regression model, generalized regression model, Ridge estimator, Liu estimator, KL estimator.

Introduction

A special case of the Generalized Linear Models (GLM) is the Poisson Regression Model (PRM) which is generally applied for count or frequency data modelling. It is employed to model the relationship between a response variable and one or more independent variable where the response variable denotes a rare event or count data. The response variable also takes the form of a non-negative variable, and it is applicable in the following fields: economics, health, social and physical sciences. The Maximum Likelihood Estimation (MLE) method is popularly used to estimate the regression coefficient in a PRM. In both a Linear Regression Model (LRM) and Generalized Linear Model (GLM), MLE suffers a setback when the independent variables are correlated, which implies multicollinearity. Multicollinearity effects include large variance and regression coefficient covariances, negligible t-ratio and a high coefficient of determination (R-square) values. Alternative estimators to the MLE in the linear regression model include the ridge regression estimator by Hoerl and Kennard (1970), Liu estimator by Liu (1993), Liu-type estimator by Liu (2003), two-parameter estimator by Özkale and Kaciranlar (2007), r-d class estimator Kaçiranlar and Sakallioǧlu (2007), k-d class estimator Sakallioglu and Kaciranlar (2008), a two-parameter estimator by Yang and Chang (2010), modified two-parameter estimator by Dorugade (2014), modified ridge-type estimator by Lukman et al. (2019), modified Liu estimator by Lukman et al. (2020), Kibria-Lukman (KL) estimator by Kibria and Lukman (2020), modified new two-parameter estimator by Ahmad and Aslam (2020), the modified Liu ridge type estimator by Aslam and Ahmad (2020) and the DK estimator by Dawoud and Kibria (2020) among others. Researchers have extended some of these existing estimators in LRM to the PRM. Mansson et al. (2012) introduced the Liu estimator into the PRM. The modified jackknifed ridge estimator for the PRM was introduced by Türkan and Özel (2016). The ridge estimator was introduced into the PRM by Månsson and Shukur (2011). A new two-parameter for PRM was developed by Asar and Genç (2017). Recently, Poisson KL estimator was developed by Lukman et al. (2021) for combating multicollinearity in the PRM.

In this study, we modified the KL estimator to handle multicollinearity in PRM. Furthermore, we compared the performance of the estimator with the Poisson Maximum Likelihood Estimator (PMLE), Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL estimator (PKLE).

Methods

Given that the response variable, yi is in the form of count data, then it is assumed to follow a Poisson distribution as Po (μi) where μi = exp (xiβ), such that xi is the ith row of X which is a n×(p+1) data matrix with p independent variables and β is a (p+11 vector of coefficients. The log likelihood of the model is given as:

(2.1)
lμy=i=1nexpxiβ+i=1nyilogexpxiβ+logi=1nyi!

The most common method of maximizing the likelihood function is to use the iterated weighted least squares (IWLS) algorithm which results to:

(2.2)
β̂MLE=XV̂X1XV̂ẑ

where V̂=diagμ̂i and ẑ is a vector while the ith element equals z^i=log(μ^i)+yiμ^iμ^i.

The MLE is normally distributed with a covariance matrix that is equivalent to the inverse of the second derivative as:

(2.3)
Covβ̂MLE=E2lβjβk1=XV̂X1

and the mean square error is given as:

Eβ̂MLE=Eβ̂MLEββ̂MLEβ=trXV̂X1=j=1P1λj

where λj is the jth eigen value of the XV̂X matrix.

The Poisson Ridge Estimator (PRE) was introduced by Månsson and Shukur (2011) as a solution to multicollinearity in PRM. The estimator is defined as follows:

(2.4)
β̂PRE=XV̂X+kI1XV̂Xβ̂MLE

where k=max1mi and σ̂2αi2

The mean square error (MSE) is:

(2.5)
MSEβ̂PRE=j=1pλjλj+k2+k2j=1pα̂j2λj+k2

Mansson et al. (2012) developed the Liu estimator to the Poisson regression model as:

(2.6)
β̂PLE=XV̂X+I1XV̂X+dβ̂β̂ML

where

(2.7)
d̂=max0α̂j21α̂j2+1λj

The means square error for the Liu estimator is defined as:

(2.8)
MSEβ̂PLE=j=1Pλj+d2λjλj+12+d12j1Jαj2λj+12

where λj is the jth eigenvalue of XV̂X and αj is the jth element of α.

The KL estimator was proposed by Kibria and Lukman (2020) as a means of mitigating the effect of multicollinearity on parameter estimation. The estimator is defined as

(2.9)
β̂KL=XX+k1XXkβ̂MLE

By means of extension, the Poisson K-L estimator was proposed by Lukman et al. (2021) as follows:

(2.10)
βPKL=XV̂X+k1XV̂Xkβ̂MLE
(2.11)
MSEβ̂PKL=j=1pλjk2λjλj+k2+4k2j=1pαj2λj+k2

The Poisson Modified KL estimator (PMKL)

The proposed estimator is obtained as follows: β̂MLE in equation (2.10) is replaced with the ridge estimator. Thus, we have:

(2.12)
βMKL=XX+k1XXkXX+k1Xy

The properties of the new estimator include:

(2.13)
Eβ̂MKL=XX+kI1XXkIXX+kI1X
Biasβ̂MKL=XX+kI1XXkIXX+kI1Xβ
(2.14)
=XX+kI2k3XXkIβ

The bias can be written in scalar form as:

(2.15)
Biasβ̂MKL=kj=1p3λjkβλj+k2
(2.16)
Vβ̂MKL=σ2XX+kI1XXkIXX+kI1XXXX+kI1XXkIXX+kI1

Vβ̂MKL can be represented in scalar form as follows:

(2.17)
Vβ̂MKL=j=1pλjλjk2λj+k4

Thus, the MSE is obtained as:

(2.18)
MSEβ̂MKL=σ2j=1pλjλjk2λj+k4+k2j=1p3λj+k2β2λj+k4

The proposed estimator in (2.13) is extended to the PRM. It is referred to as the Poisson modified KL (PMKL) estimator and defined as:

(2.19)
β̂PMKL=XV̂X+k1XV̂XkXV̂X+k1XV̂Xβ̂MLE

The mean square error of the PMKL is defined as:

(2.20)
MSEβ̂PMKL=j=1pλjλjk2λj+k4+k2j=1p3λj+k2αj2λj+k4

The following lemmas are adopted for theoretical comparisons among the estimators.

Lemma 2.1 Let A be a positive definite (pd) matrix, that is, A > 0, and a be some vector, then Aaa'0 if and only if (iff) a'A1a1 (Farebrother, 1976).

Lemma 2.2 MSEM(β^1)MSEM(β^2)=σ2D+b1b2b2b2>0

if and only if b2[σ2D+b1b1]1b2<1 where MSE(β^j)=Cov(β^j)+bjbj (Trenker and Toutenburg, 1990).

Theorem 2.1: α̂PMKL is preferred to α^PMLE iff, MSEMα̂MLEMSEMα̂PMKL>0 provided k > 0.

Proof

Vα̂PMLEVα̂PMKL=Qdiag1λjλjλjk2λj+k4j=1PQ

It is observed that λj+k4λj2λjk2>0 such that the expression above is non-negative for k > 0

Theorem 2.2: α̂PMKL is preferred to α̂PRE iff, MSEMα̂PREMSEMα̂PMKL>0 provided k > 0.

Proof

Vα̂PREVα̂PMKL=Qdiagλjλj+k2λjλjk2λj+k4j=1pQ

We can observe that the difference of the variance of the estimator is non-negative since λj+k2λjλjλjk2>0 for k > 0.

Theorem 2.3: α̂PMKL is preferred to α̂PLE iff, MSEMα̂PLEMSEMα̂PMKL>0 provided k > 0 and 0 < d < 1.

Proof

Vα̂PLECovα̂PMKL=Qdiagλj+d2λjλj+12λjλjk2λj+k4j=1pQ

The difference of the variance is non-negative since

λj+kλj+dλjλj+1λjk>0 for 0 < d < 1 and k > 0.

Theorem 2.4: α̂PMKL is preferred to α̂PKL iff, MSEMα̂PKLMSEMα̂PMKL>0 provided k > 0.

Proof

Vα̂PKLCovα̂PMKL=Qdiagλjk2λjλj+k2λjλjk2λj+k4j=1pQ

The difference of the variance is non-negative since λj+kλjkλjλjk>0 for k > 0.

Selection of biasing parameter

The biasing parameter k for the estimator is obtained by differentiating the MSE with respect to k and obtained as:

(2.21)
kMKL=min3λiα2+σ22+4α2σ2λi3λiα2+σ22β23λiα2+σ2

The shrinkage parameter estimated by Mansson and Shukur, (2011) and Kibria and Lukman (2020) was also adopted for this study as listed:

(2.22)
k1=1maxαj2
(2.23)
k2=p2αj2+1e
(2.24)
k3=minλi2λjαj2+1

k1 is the biasing parameter for PMKL1, while k2 and k3 are the biasing parameters for PMKL2 and PMKL3.

Simulation Design and Real-Life Application

Simulation study and result

In this section, a simulation study is carried out to compare the performance of the different estimators. The generation of the dependent variables are done using pseudo-random numbers from Po (μi) where μi=eβxii=1,2,,n and Xi is the ith row of the design matrix with β=β0β1βp being the coefficient vector. The generation of the independent variables with different levels of correlation is obtained using

(3.1)
xij=1ρ212zij+ρzip

where ρ is the level of multicollinearity between the independent variables (Kibria et al. 2015; Kibria and Banik, 2016; Lukman et al., 2019b, Lukman et al. 2020b). zij are pseudo-random numbers generated using the standard normal distribution such that i ranges from 1 to n and j from 1 to p. As a common restriction used in simulation studies, it is assumed that j=1pβj2=1 and β1=β2==βp. Also, the effect of the intercept value is also being investigated as values are taken to be 1, 0 and -1 (Kibria et al. 2014). The different levels of correlation taken are 0.8, 0.9, 0.95, 0.99 and 0.999. The other factors varied in the simulation study are the sample size n and the number of independent variable p. We assume n = 50, 100 and 200 observations and p = 4 and 8 independent variables.

The simulation results in Tables 1 to 6 that for each of the estimators, the simulated MSE values increase as the multicollinearity level increases, keeping other factors constant. There is also an increase in the mean square error as the sample size increases for all estimators compared while other factors were kept constant. As the intercept values varied from -1 to +1, the values of the mean square error reduced for all estimators. Result shows that the PMKL1 performed best with minimum MSE at varying sample sizes. It was closely followed by PMKL2. They are both considered more suitable for estimation of parameters in the Poisson regression model than the MLE as it performed worst when multicollinearity is a challenge. In general, the PMKL1 estimator consistently performed more efficiently than the MLE, PRE, PLE and the PKL estimators.

Table 1. Simulation result for mean square error (MSE) when P = 4 and intercept = 1.

β0NρMLEPREPLEPKLPMKL1PMKL2PMKL3
1500.80.03890.03760.03840.03830.03660.03660.0383
0.90.05340.04940.05200.05150.04400.04460.0515
0.950.08520.07290.08080.07910.05530.05740.0806
0.990.35480.20130.28000.24350.06960.07500.2835
0.9993.42440.87571.81910.23020.11870.10411.4952
1000.80.01070.01080.01070.01070.01130.01110.0107
0.90.01250.01240.01250.01250.01250.01250.0125
0.950.01820.01770.01810.01810.01710.01720.0181
0.990.06910.05950.06700.06650.04510.04800.0682
0.9990.64650.29950.50610.44040.08520.09790.6098
2000.80.00570.00560.00560.00560.00600.00590.0056
0.90.00680.00680.00680.00680.00670.00680.0067
0.950.01050.01040.01050.01050.01030.01040.0105
0.990.04220.03940.04160.04150.03470.03570.0419
0.9990.52340.18970.43550.23220.04190.03240.5211

Table 2. Simulation result for mean square error (MSE) when P = 4 and intercept = 0.

β0nρMLEPREPLEPKLPMKL1PMKL2PMKL3
0500.80.10910.08210.10030.10000.04740.05320.1036
0.90.14790.09780.13030.12950.05050.05560.1386
0.950.23560.12870.19100.18660.05360.05880.2158
0.990.93930.28470.54100.34250.05650.05930.6883
0.9999.47571.93494.80302.21840.25620.18172.9303
1000.80.02950.02660.02910.02910.02300.02380.0294
0.90.03400.03010.03350.03350.02430.02580.0339
0.950.05000.04160.04880.04880.02970.03250.0497
0.990.18960.10880.17120.17060.04200.05030.1867
0.9993.56241.18971.54320.71680.09450.09911.5706
2000.80.01540.01530.01530.01530.01380.01390.0154
0.90.01780.01870.01870.01870.01610.01660.0188
0.950.02620.02840.02840.02840.02230.02340.0286
0.990.82920.10830.10830.10820.04540.05290.1126
0.9991.51850.32220.85480.11830.05430.07430.9527

Table 3. Simulation result for mean square error (MSE) when P = 4 and intercept = -1.

β0nρMLEPREPLEPKLPMKL1PMKL2PMKL3
-1500.80.30890.22300.25690.22110.20930.21460.2478
0.90.42950.26040.33270.27020.22990.23660.3205
0.950.69240.33720.48900.33640.25620.27550.4573
0.992.78020.77231.51140.33020.30200.37341.0038
0.99926.97265.492814.10599.20160.74861.93804.1156
1000.80.08090.07720.07750.07640.05330.07390.0778
0.90.09350.08340.08910.08860.10430.10040.0812
0.950.13890.11160.12900.12870.11750.10990.1296
0.990.51610.26660.40240.38780.12810.12310.4790
0.9994.68051.12572.67750.75170.19940.20433.5989
2000.80.04210.04260.04170.04090.04040.04230.0409
0.90.05110.04980.05010.04980.04460.05350.0499
0.950.07670.07040.07410.07410.06720.06330.0741
0.990.31070.21150.27660.27310.12260.13090.3016
0.9994.12470.84502.02750.71020.13690.15282.6541

Table 4. Simulation result for mean square error (MSE) when P = 8 and intercept = 1.

β0nρMLEPREPLEPKLPMKL1PMKL2PMKL3
1500.80.09800.08830.09600.09510.07960.08040.0969
0.90.14040.11630.13550.13290.09060.09380.1369
0.950.22560.16310.21130.20360.10040.10720.2201
0.990.92550.39560.71900.58370.11330.12560.8598
0.9998.47131.94144.88161.04530.30700.27885.8787
1000.80.02320.02290.02310.02310.02270.02270.0231
0.90.03400.03290.03370.03350.03140.03160.0336
0.950.05340.04990.05250.05180.04400.04490.0526
0.990.22260.16340.20260.18910.08530.07920.2081
0.9992.11850.72841.29500.44990.08820.09511.2901
2000.80.00570.00570.00570.00570.00560.00570.0057
0.90.00760.00760.00760.00760.00750.00740.0076
0.950.01170.01150.01160.01160.011300.011340.0116
0.990.04430.04120.04340.04300.03560.03650.0436
0.9991.87220.20710.54910.26710.05660.05620.8730

Table 5. Simulation result for mean square error (MSE) when P = 8 and intercept = 0.

β0nρMLEPREPLEPKLPMKL1PMKL2PMKL3
0500.80.27380.14730.23770.23520.08080.08790.2682
0.90.39270.18340.32580.32010.08290.08990.3825
0.950.61140.23820.46770.44480.09420.09520.5888
0.992.48580.62181.48820.78650.12050.14282.1778
0.99923.35484.686013.25736.52910.70550.463612.7807
1000.80.06460.05540.06170.06170.04910.04910.0635
0.90.09340.07500.08790.08780.05260.05780.0907
0.950.14620.10410.13270.13240.05530.05930.1395
0.990.60680.25890.42340.25770.05870.06430.5154
0.9995.68541.41782.99510.38780.14330.10702.2332
2000.80.01590.01510.01570.01570.01410.01430.0158
0.90.02070.01960.02050.02050.01760.01810.0206
0.950.03190.02900.03120.03120.02420.02540.0315
0.990.11850.08570.10890.10870.04410.05060.1141
0.9991.75960.46121.08890.18100.07940.09211.0001

Table 6. Simulation result for mean square error (MSE) when P = 8 and intercept = -1.

β0nρMLEPREPLEPKLPMKL1PMKL2PMKL3
-1500.80.82480.2590.64690.45120.21590.67000.7314
0.91.13550.49450.82530.56990.46930.55260.9927
0.951.77010.62641.17450.69210.58480.56141.4256
0.997.19641.68654.20931.00610.47350.44934.8258
0.99965.876012.825138.072637.73052.04090.901724.8129
1000.80.18000.15420.16530.14930.14220.16160.1547
0.90.25750.20010.22690.20730.20200.18580.2131
0.950.41050.27290.33920.30190.23540.21150.3419
0.991.69140.65361.09830.50390.24200.21801.1012
0.99915.56673.71888.36204.05650.37100.22213.8870
2000.80.04360.04220.04250.04190.03030.04750.0419
0.90.05680.05350.05460.05440.05120.05450.0542
0.950.08600.07660.08100.08080.06930.06910.0813
0.990.32600.21680.27530.25620.10220.10870.2890
0.9998.75941.56393.32662.00650.24330.16211.7855

Real Life Application

Having carried out a simulation study, the efficacy of the proposed estimator needs to be further investigated by considering a real-life application. The Poisson regression model has been applied to the aircraft damage dataset initially by Myers et al. (2012) and subsequently by other researchers such as Asar and Genc (2017) and Amin et al. (2020) among others. By following the Pearson chi-square goodness of fit test, Amin et al. (2020) was able to ascertain that the data fits a Poisson regression model. The test confirms the suitability of the response variable to Poisson distribution with P-value of 6.898122 (0.07521). The dataset provides some detail on two separate aircrafts: The McDonnell Douglas A-4 Skyhawk and the A-6 Grumman Itruder. The dependent variable denotes the number of locations with damage on the aircraft and this follows a Poisson distribution (Asar and Genc, 2017; Amin et al., 2020). The data set has three explanatory variables, X1 shows the type of aircraft which makes the outcome binary (A-4 is coded as 0 and A-6 is coded as 1). X2 is the bomb load in tons and X3 is the number of months of aircrew experience. Meyers et al. (2012) was able to ascertain that the data set is greatly affected by multicollinearity. The eigenvalues of the matrix X were obtained as 4.3333, 374.8961 and 2085.2251. The condition number of 219.3654 was also obtained which is an indication of the problem of multicollinearity since it is greater than 30 (Asar and Genc, 2017). The performance of the estimators is judged based on the mean square error of each of the estimators.

From Table 7, it is evident that all of the regression coefficients had identical signs. The estimator with the highest mean squared error is the MLE due to the presence of multicollinearity. The suggested estimator (PMKL1, PMKL2, PMKL3) has the lowest MSE that has established its dominance. We also observed that the performance of the estimator is highly dependent on the biasing parameter k.

Table 7. Regression coefficients and MSE.

coef.MLEPREPLEPKLPMKL1
(k1)
PMKL2
(k2)
PMKL3
(k3)
α^0-0.406-0.167-0.255-0.107-0.019-0.002-0.077
α^10.5690.3800.4790.3910.1200.1790.322
α^20.1650.1710.1670.1680.1830.1790.172
α^3-0.014-0.015-0.015-0.016-0.017-0.017-0.016
MSE1.0290.2730.4320.2250.0830.0950.092

Conclusion

The parameters in the PRM are commonly estimated using the Maximum Likelihood Estimator. However, literature had shown that the estimator suffers a setback when the explanatory variables are correlated. This problem led to the implementation of alternative estimators with single shrinkage parameters such as the Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL Estimator (PKLE). The KL estimator was generally preferred to the ridge regression and Liu estimator in the linear regression model. According to Lukman et al. (2021), the Poisson KL estimator outperforms PRE and PLE. This study modified the KL estimator to propose a new estimator called the Poisson Modified KL estimator (PMKL). The new estimator falls in the same class with the ridge, Liu and KL estimators since they possessed a single shrinkage parameter. We investigated the performance of the estimators with a simulation study and a real-life application. From the results, we observed that the new estimator consistently performed well in the presence of multicollinearity with the lowest MSE. Finally, the new estimator is more suitable to combat multicollinearity in the PRM.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.

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Aladeitan BB, Adebimpe O, Lukman AF et al. Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 1; peer review: 3 approved with reservations]. F1000Research 2021, 10:548 (https://doi.org/10.12688/f1000research.53987.1)
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Reviewer Report 09 Aug 2021
Nimet Özbay, Department of Statistics, Faculty of Science and Letters, Çukurova University, Adana, Turkey 
Approved with Reservations
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This article focuses on proposing a modified KL estimator to mitigate the Poisson Regression Model with multicollinearity. Some theoretical properties of the new estimator are examined. A numerical example is conducted to show the performance of the new estimator. I ... Continue reading
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Özbay N. Reviewer Report For: Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 1; peer review: 3 approved with reservations]. F1000Research 2021, 10:548 (https://doi.org/10.5256/f1000research.57427.r89263)
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Reviewer Report 03 Aug 2021
Muhammad Amin, Department of Statistics, University of Sargodha, Sargodha, Pakistan 
Approved with Reservations
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In this paper, the authors introduced a new estimator by modified KL estimator for the Poisson regression model to overcome the effect of multicollinearity. The paper is original and deals with a topic of interest. This paper could be accepted ... Continue reading
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Amin M. Reviewer Report For: Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 1; peer review: 3 approved with reservations]. F1000Research 2021, 10:548 (https://doi.org/10.5256/f1000research.57427.r89258)
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Reviewer Report 21 Jul 2021
Mohammad Arashi, Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran 
Approved with Reservations
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The paper extends the Liu estimator in generalized linear modeling. Specifically, the authors propose a new biased estimator for the estimation of regression coefficients in the discrete Poisson regression.

The results are interesting and the topic is ... Continue reading
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Arashi M. Reviewer Report For: Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 1; peer review: 3 approved with reservations]. F1000Research 2021, 10:548 (https://doi.org/10.5256/f1000research.57427.r89260)
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  • Author Response 27 Jul 2021
    BENEDICTA Aladeitan, Department of Physical Sciences, Landmark University, Omu-Aran, +234, Nigeria
    27 Jul 2021
    Author Response
    Thanks for your observations and corrections. All will be duely implemented.
    Competing Interests: No competing interests were disclosed.
COMMENTS ON THIS REPORT
  • Author Response 27 Jul 2021
    BENEDICTA Aladeitan, Department of Physical Sciences, Landmark University, Omu-Aran, +234, Nigeria
    27 Jul 2021
    Author Response
    Thanks for your observations and corrections. All will be duely implemented.
    Competing Interests: No competing interests were disclosed.

Comments on this article Comments (0)

Version 2
VERSION 2 PUBLISHED 08 Jul 2021
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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