Open Access

Performances of Shannon’s Entropy Statistic in Assessment of Distribution of Data


Cite

[1]. A. Kolmogorov, Sulla determinazione empirica di una legge di distribuzione, Giornale dell'Istituto Italiano degli Attuari 4 (1933) 83-91. Search in Google Scholar

[2]. N. Smirnov, Table for estimating the goodness of fit of empirical distributions, Annals of Mathematical Statistics 19 (1948) 279-281. 10.1214/aoms/1177730256Open DOISearch in Google Scholar

[3]. T.W. Anderson, D.A. Darling, Asymptotic theory of certain "goodness-of-fit" criteria based on stochastic processes, Annals of Mathematical Statistics 23 (1952) 193-212. DOI: 10.1214/aoms/1177729437Open DOISearch in Google Scholar

[4]. T.W. Anderson, D.A. Darling, A Test of Goodness-of-Fit, Journal of the American Statistical Association 49 (1954) 765-769. 10.1080/01621459.1954.10501232Search in Google Scholar

[5]. K. Pearson, Contribution to the mathematical theory of evolution, II. Skew variation in homogenous material, Philosophical Transactions of the Royal Society of London 91 (1895) 343-414. DOI: 10.1098/rsta.1895.0010 10.1098/rsta.1895.0010Open DOISearch in Google Scholar

[6]. K. Pearson, On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling, Philosophical Magazine Series 5 50 (1900) 157-175. DOI: 10.1080/14786440009463897 10.1080/14786440009463897Open DOISearch in Google Scholar

[7]. H. Cramér, On the composition of elementary errors, Skand Akt. 11 (1928) 141-180. DOI: 10.1080/03461238.1928.10416872 10.1080/03461238.1928.10416872Open DOISearch in Google Scholar

[8]. R.E. von Mises, Wahrscheinlichkeit, Statistik und Wahrheit, Julius Springer, Vienna, Austria (1928). 10.1007/978-3-662-36230-3Search in Google Scholar

[9]. S.S. Shapiro, M.B. Wilk, An analysis of variance test for normality (complete samples), Biometrika 52 (1965) 591-611. DOI: 10.2307/2333709 10.2307/2333709Open DOISearch in Google Scholar

[10]. C.M. Jarque, A.K. Bera, Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters 6 (1980) 255-259. DOI: 10.1016/0165-1765(80)90024-5 10.1016/0165-1765(80)90024-5Search in Google Scholar

[11]. C.M. Jarque, A.K. Bera, Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence, Economics Letters 7 (1981) 313-318. DOI: 10.1016/0165-1765(81)90035-5 10.1016/0165-1765(81)90035-5Open DOISearch in Google Scholar

[12]. C.M. Jarque, A.K. Bera, A test for normality of observations and regression residuals, International Statistical Review 55 (1987) 163-172. DOI: 10.2307/1403192 10.2307/1403192Open DOISearch in Google Scholar

[13]. R.B. D’Agostino, A. Belanger, R.B.Jr. D’Agostino, A suggestion for using powerful and informative tests of normality, The American Statistician 44 (1990) 316-321. DOI: 10.2307/2684359 10.2307/2684359Open DOISearch in Google Scholar

[14]. H.W. Lilliefors, On the Kolmogorov-Smirnov for normality with mean and variance unknown, Journal of the American Statistical Association 62 (1967) 399-402. DOI: 10.2307/2283970 10.2307/2283970Open DOISearch in Google Scholar

[15]. S.S. Shapiro, R.S. Francia, An approximate analysis of variance test for normality, Journal of the American Statistical Association 67 (1972) 215-216. 10.1080/01621459.1972.10481232Search in Google Scholar

[16]. I.M. Chakravarti, R.G. Laha, J. Roy, Handbook of Methods of Applied Statistics, John Wiley and Sons 1 (1967) 392-394. Search in Google Scholar

[17]. T.B. Arnold, J.W. Emerson, Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions, The R Journal 3/2 (2011) 34-39. 10.32614/RJ-2011-016Search in Google Scholar

[18]. A. DasGupta, Asymptotic theory of statistics and probability, Springer, New York (2008). Search in Google Scholar

[19]. C. Walck, Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists. University of Stockholm: Internal Report SUF-PFY/96-01, 1996, last modification 10 September 2007. Available online: http://www.stat.rice.edu/~dobelman/textfiles/DistributionsHandbook.pdf (accessed on 27 March 2017) Search in Google Scholar

[20]. D. Curran-Everett, D.J. Benos, Guidelines for reporting statistics in journals published by the American Physiological Society, American Journal of Physiology. Endocrinology and Metabolism 287 (2004) E189-91. DOI: 10.1152/ajpendo.00213.2004 10.1152/ajpendo.00213.200415271643Open DOISearch in Google Scholar

[21]. T.A. Lang, D.G. Altman, Basic Statistical Reporting for Articles Published in Biomedical Journals: The “Statistical Analyses and Methods in the Published Literature” or The SAMPL Guidelines”. In: Smart, P.; Maisonneuve, H.; Polderman, A. (Eds). Science Editors' Handbook, European Association of Science Editors, 2013. Available online: http://www.equator-network.org/wp-content/uploads/2013/07/SAMPL-Guidelines-6-27-13.pdf (accessed on 27 March 2017) Search in Google Scholar

[22]. B.M. Cesana, F. Cavaliere, Basics to perform and present statistical analyses in scientific biomedical reports Part 1, Minerva Anestesiologica 82 (2016) 822-826. Search in Google Scholar

[23]. N.M. Razali, Y.B. Wah, Power comparison of Shapiro-Wilk, Kolmogorov-Smirnow, Lilliefors and Anderson-darling tests, Journal of Statistical Modeling and Analytics 2 (2011) 21-33. Search in Google Scholar

[24]. I. Tui, Normality Testing - A New Direction, International Journal of Business and Social Sciences 2 (2011) 115-118. Search in Google Scholar

[25]. T.U. Islam, Stringency-based ranking of normality tests, Communications in Statistics: Simulation and Computation 46 (2017) 655-668. DOI: 10.1080/03610918.2014.977916 10.1080/03610918.2014.977916Open DOISearch in Google Scholar

[26]. A.K. Mbah, A. Paothong, Shapiro-Francia test compared to other normality test using expected p-value, Journal of Statistical Computation and Simulation 85 (2015) 3002-3016. DOI: 10.1080/00949655.2014.947986 10.1080/00949655.2014.947986Open DOISearch in Google Scholar

[27]. D. He, X. Xu, A goodness-of-fit testing approach for normality based on the posterior predictive distribution, Test 22 (2013) 1-18. DOI: 10.1007/s11749-012-0282-6 10.1007/s11749-012-0282-6Open DOISearch in Google Scholar

[28]. A.K. Bera, A.F. Galvao, L. Wang, Z. Xiao, A New Characterization of the Normal Distribution and Test for Normality, Econometric Theory 32 (2016) 1216-1252. DOI: 10.1017/S026646661500016X 10.1017/S026646661500016XOpen DOISearch in Google Scholar

[29]. H. Torabi, N.H. Montazeri, A. Grane, A test for normality based on the empirical distribution function, SORT 40 (2016) 55-87. DOI: 10.1007/978-3-642-04898-2_591 10.1007/978-3-642-04898-2_591Open DOISearch in Google Scholar

[30]. B. Choi, K. Kim, Testing goodness-of-fit for Laplace distribution based on maximum entropy, Statistics 40 (2006) 517-531. DOI: 10.1080/02331880600822473 10.1080/02331880600822473Open DOISearch in Google Scholar

[31]. A. Batsidis, P. Economou, G. Tzavelas, Tests of fit for a lognormal distribution, Journal of Statistical Computation and Simulation 86 (2016) 215-235. DOI: 10.1080/00949655.2014.1003138 10.1080/00949655.2014.1003138Open DOISearch in Google Scholar

[32]. T. Ledwina, G. Wyłupek, Detection of non-Gaussianity, Journal of Statistical Computation and Simulation 85 (2015) 3480-3497. DOI: 10.1080/00949655.2014.983110 10.1080/00949655.2014.983110Open DOISearch in Google Scholar

[33]. G.J. Székely, M.L. Rizzo, A new test for multivariate normality, Journal of Multivariate Analysis 93 (2005) 58-80. DOI: 10.1016/j.jmva.2003.12.002 10.1016/j.jmva.2003.12.002Open DOISearch in Google Scholar

[34]. E. Zamanzade, N.R. Arghami, Testing normality based on new entropy estimators, Journal of Statistical Computation and Simulation 82 (2012) 1701-1713. DOI: 10.1080/00949655.2011.592984 10.1080/00949655.2011.592984Open DOISearch in Google Scholar

[35]. O. Vasicek, A test for normality based on sample entropy, Journal of the Royal Statistical Society. Series B (Methodological) 38 (1976) 54-59. 10.1111/j.2517-6161.1976.tb01566.xSearch in Google Scholar

[36]. P. Prescott, On a Test for Normality Based on Sample Entropy, Journal of the Royal Statistical Society. Series B (Methodological) 38 (1976) 254-256. 10.1111/j.2517-6161.1976.tb01590.xSearch in Google Scholar

[37]. J.C. Correa, A new estimator of entropy, Communications in Statistics - Theory and Methods 24 (1995) 2439-2449. DOI: 10.1080/03610929508831626 10.1080/03610929508831626Open DOISearch in Google Scholar

[38]. P. Crzcgorzewski, R. Wirczorkowski, Entropy-based goodness-of-fit test for exponentiality, Communications in Statistics - Theory and Methods 28 (1999) 1183-1202. DOI: 10.1080/03610929908832351 10.1080/03610929908832351Open DOISearch in Google Scholar

[39]. H.A. Noughabi, An estimator of entropy and its application in testing normality, Journal of Statistical Computation and Simulation 80 (2010) 1151-1162. DOI: 10.1080/00949650903005656 10.1080/00949650903005656Open DOISearch in Google Scholar

[40]. M. Bitaraf, M. Rezaei, F. Yousefzadeh, Test for normality based on two new estimators of entropy, Journal of Statistical Computation and Simulation 87 (2017) 280-294. DOI: 10.1080/00949655.2016.1208201 10.1080/00949655.2016.1208201Open DOISearch in Google Scholar

[41]. S. Park, A goodness-of-fit test for normality based on the sample entropy of order statistics, Statistics & Probability Letters 44 (1999) 359-363. DOI: 10.1016/S0167-7152(99)00027-9 10.1016/S0167-7152(99)00027-9Open DOISearch in Google Scholar

[42]. H.A. Noughabi, Two Powerful Tests for Normality, Annals of Data Science 3 (2016) 225-234. DOI: 10.1007/s40745-016-0083-y 10.1007/s40745-016-0083-yOpen DOISearch in Google Scholar

[43]. S. Lee, A maximum entropy type test of fit: Composite hypothesis case, Computational Statistics & Data Analysis 57 (2013) 59-67. DOI: 10.1016/j.csda.2012.06.006 10.1016/j.csda.2012.06.006Open DOISearch in Google Scholar

[44]. E.J. Dudewicz, E.C. van der Meulen, Entropy-Based Tests of Uniformity, Journal of the American Statistical Association 76 (1981) 967-974. DOI: 10.2307/2287597 10.2307/2287597Open DOISearch in Google Scholar

[45]. E. Zamanzade, N.R. Arghami, Testing normality based on new entropy estimators, Journal of Statistical Computation and Simulation 82 (2012) 1701-1713. DOI: 10.1080/00949655.2011.592984 10.1080/00949655.2011.592984Open DOISearch in Google Scholar

[46]. E. Zamanzadea, Testing uniformity based on new entropy estimators, Journal of Statistical Computation and Simulation 85 (2014) 3191-3205. DOI: 10.1080/00949655.2014.958085 10.1080/00949655.2014.958085Open DOISearch in Google Scholar

[47]. S. Lee, I. Vonta, A. Karagrigoriou, A maximum entropy type test of fit, Computational Statistics and Data Analysis 52 (2011) 2635-2643. DOI: 10.1016/j.csda.2011.03.012 10.1016/j.csda.2011.03.012Open DOISearch in Google Scholar

[48]. D.V. Gokhale, On entropy-based goodness-of-fit tests, Computational Statistics & Data Analysis 1 (1983) 157-165. DOI: 10.1016/0167-9473(83)90087-7 10.1016/0167-9473(83)90087-7Open DOISearch in Google Scholar

[49]. S. Lee, Goodness of fit test for discrete random variables, Computational Statistics & Data Analysis 69 (2014) 92-100. DOI: 10.1016/j.csda.2013.07.026 10.1016/j.csda.2013.07.026Open DOISearch in Google Scholar

[50]. J. Lequesne, Entropy-based goodness-of-fit test: Application to the Pareto distribution, AIP Conference Proceedings 1553 (2013) 155-162. DOI: 10.1063/1.4819995 10.1063/1.4819995Open DOISearch in Google Scholar

[51]. B. Afhami, M. Madadi, Entropy-based goodness-of-fit tests for the Pareto I distribution, Communications in Statistics - Theory and Methods 46 (2017) 3649-3666. DOI: 10.1080/03610926.2015.1069350 10.1080/03610926.2015.1069350Open DOISearch in Google Scholar

[52]. S. Lee, M. Kim, On entropy-based goodness-of-fit test for asymmetric Student-t and exponential power distributions, Journal of Statistical Computation and Simulation 87 (2017) 187-197. DOI: 10.1080/00949655.2016.1196690 10.1080/00949655.2016.1196690Open DOISearch in Google Scholar

[53]. L. Jäntschi, S.D. Bolboacă, Distribution fitting 2. Pearson-Fisher, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, Kramer-von-Misses and Jarque-Bera statistics, Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Horticulture 66 (2009) 691-697. Search in Google Scholar

[54]. R.A. Fisher, Questions and answers #14, The American Statistician 2 (1948) 30-31. Search in Google Scholar

[55]. S.D. Bolboacă, L. Jäntschi, A.F. Sestraş, R.E. Sestraş, Pamfil, D.C. Supplementary material of 'Pearson-Fisher chi-square statistic revisited', Information 2 (2011) 528-545. DOI: 10.3390/info2030528 10.3390/info2030528Open DOISearch in Google Scholar

[56]. C.E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948) 379-423. DOI: 10.1002/j.1538-7305.1948.tb01338.x 10.1002/j.1538-7305.1948.tb01338.xOpen DOISearch in Google Scholar

[57]. M. Matsumoto, T. Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Transactions on Modeling and Computer Simulation 8 (1998) 3-30. 10.1145/272991.272995Search in Google Scholar

[58]. N.H. Kuiper, Tests concerning random points on a circle, Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A 63 (1960) 38-47. DOI: 0.1016/S1385-7258(60)50006-0 Search in Google Scholar

[59]. J. Zar, Biostatistical analysis, 2nd ed, Prentice-Hall, Inc., Englewood Cliffs, NJ, USA (1984). Search in Google Scholar

[60]. I. Mitra, A. Saha, K. Roy, Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols, Scientia Pharmaceutica 79 (2011) 31-57. DOI: 10.3797/scipharm.1011-02. 10.3797/scipharm.1011-02309750121617771Open DOISearch in Google Scholar

[61]. C. Cena, D. Boschi, G.C. Tron, K. Chegaev, L. Lazzarato, A. Di Stilo, M. Aragno, R. Fruttero, A. Gasco, Development of a new class of potential antiatherosclerosis agents: NO-donor antioxidants, Bioorganic & Medicinal Chemistry Letters 14 (2004) 5971-5974. DOI: 10.1016/j.bmcl.2004.10.006 10.1016/j.bmcl.2004.10.00615546710Open DOISearch in Google Scholar

[62]. S.D. Bolboacă, L. Jäntschi, Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds, International Journal of Molecular Science 12 (2011) 4348-4364. DOI: 10.3390/ijms12074348 10.3390/ijms12074348315535521845082Open DOISearch in Google Scholar

[63]. J. Li, P. Gramatica, The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders, Molecular Diversity 14 (2010) 687-696. DOI: 10.1007/s11030-009-9212-2 10.1007/s11030-009-9212-2Open DOISearch in Google Scholar

[64]. F. Gharagheizi, A simple equation for prediction of net heat of combustion of pure chemicals, Chemometrics and Intelligent Laboratory Systems 91 (2008) 177-180. DOI: 10.1016/j.chemolab.2007.11.003 10.1016/j.chemolab.2007.11.003Open DOISearch in Google Scholar

[65]. ChemIDPlus, ToxNet DATABSE. Available online: URL: http://chem.sis.nlm.nih.gov (accessed on 20 September 2016). Search in Google Scholar

[66]. A. Morales Helguera, M.N.D.S. Cordeiro, M.A.C. Perez, R.D. Combes, M. Perez Gonzalez, QSAR modeling of the rodent carcinogenicity of nitrocompounds, Bioorganic & Medicinal Chemistry 16 (2008) 3395-3407. DOI: 10.1016/j.bmc.2007.11.029 10.1016/j.bmc.2007.11.029Open DOISearch in Google Scholar

[67]. V. Aruoja, M. Sihtmäe, H.C. Dubourguier, A. Kahru, Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: comparison with published data and QSARs, Chemosphere 84 (2011) 1310-1320. DOI: 10.1016/j.chemosphere.2011.05.023 10.1016/j.chemosphere.2011.05.023Open DOISearch in Google Scholar

[68]. Y.H. Zhao, X. Yuan, L.M. Su, W.C. Qin, M.H. Abraham, Classification of toxicity of phenols to Tetrahymena pyriformis and subsequent derivation of QSARs from hydrophobic, ionization and electronic parameters, Chemosphere 75 (2009) 866-871. DOI: 10.1016/j.chemosphere.2009.01.055 10.1016/j.chemosphere.2009.01.055Open DOISearch in Google Scholar

[69]. R.U. Kadam, N. Roy, Cluster analysis and two-dimensional quantitative structure-activity relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors, Bioorganic & Medicinal Chemistry Letters 16 (2006) 5136-5143. DOI: 10.1016/j.bmcl.2006.07.041 10.1016/j.bmcl.2006.07.041Open DOISearch in Google Scholar

[70]. J.B. Ghasemi, R. Safavi-Sohi, E.G. Barbosa, 4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling, Molecular Diversity 16 (2012) 203-213. 10.1007/s11030-011-9340-3Open DOISearch in Google Scholar

[71]. P.R. Duchowicz, A. Talevi, L.E. Bruno-Blanch, E.A. Castro, New QSPR study for the prediction of aqueous solubility of drug-like compounds, Bioorganic & Medicinal Chemistry 16 (2008) 7944-7955. 10.1016/j.bmc.2008.07.067Search in Google Scholar

[72]. C.T. Supuran, B.W. Clare, Carbonic anhydrase inhibitors - part 57: Quantum chemical QSAR of a group of 1,3,4-thiadiazole- and 1,3,4-thiadiazoline disulfonamides with carbonic anhydrase inhibitory properties, European Journal of Medicinal Chemistry 34 (1999) 41-50. DOI: 10.1016/S0223-5234(99)80039-7 10.1016/S0223-5234(99)80039-7Open DOISearch in Google Scholar

[73]. A.T. Balaban, P.V. Khadikar, C.T. Supuran, A. Thakur, M. Thakur, Study on supramolecular complexing ability vis-à-vis estimation of pKa of substituted sulfonamides: dominating role of Balaban index (J), Bioorganic & Medicinal Cemistry Letters 15 (2005) 3966-3973. DOI: 10.1016/j.bmcl.2005.05.136 10.1016/j.bmcl.2005.05.13616046126Open DOISearch in Google Scholar

[74]. G. Melagraki, A. Afantitis, H. Sarimveis, O. Igglessi-Markopoulou, C.T. Supuran, QSAR study on para-substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices, Bioorganic & Medicinal Chemistry 14 (2006) 1108-1114. DOI: 10.1016/j.bmc.2005.09.038 10.1016/j.bmc.2005.09.038Open DOISearch in Google Scholar

[75]. E. Eroglu, Some QSAR studies for a group of sulfonamide Schiff base as carbonic anhydrase CA II inhibitors, International Journal of Molecular Sciences 9 (2008) 181-197. 10.3390/ijms9020181Open DOISearch in Google Scholar

[76]. L. Puccetti, G. Fasolis, D. Vullo, Z.H. Chohan, A. Scozzafava, C.T. Supuran, Carbonic anhydrase inhibitors. Inhibition of cytosolic/tumor-associated carbonic anhydrase isozymes I, II, IX, and XII with Schiff's bases incorporating chromone and aromatic sulfonamide moieties, and their zinc complexes, Bioorganic & Medicinal Chemistry Letters 15 (2005) 3096-3101. DOI: 10.1016/j.bmcl.2005.04.055 10.1016/j.bmcl.2005.04.055Open DOISearch in Google Scholar

[77]. C.T. Supuran, A. Scozzafava, A. Popescu, R. Bobes-Tureac, A. Banciu, G. Bobes-Tureac, M.D. Banciu, Carbonic anhydrase inhibitors. Part 43. Schiff bases derived from aromatic sulfonamides: towards more specific inhibitors for membrane-bound versus cytosolic isozymes, European Journal of Medicinal Chemistry 32 (1997) 445-452. DOI: 10.1016/S0223-5234(97)81681-9 10.1016/S0223-5234(97)81681-9Open DOISearch in Google Scholar

[78]. J. Krungkrai, A. Scozzafava, R. Reungprapavut, S.R. Krungkrai, R. Rattanajak, S. Kamchonwongpaisand, C.T. Supuran, Carbonic anhydrase inhibitors. Inhibition of Plasmodium falciparum carbonic anhydrase with aromatic sulfonamides: towards antimalarials with a novel mechanism of action, Bioorganic & Medicinal Chemistry 13 (2005) 483-489. DOI: 10.1016/j.bmc.2004.10.015 10.1016/j.bmc.2004.10.015Open DOISearch in Google Scholar

[79]. S. Mohanraj, M. Doble, 3-D QSAR Studies of Microtubule Stabilizing Antimitotic Agents Towards Six Cancer Cell Lines, QSAR & Combinatorial Science 25 (2006) 952-960. DOI: 10.1002/qsar.200630029 10.1002/qsar.200630029Open DOISearch in Google Scholar

[80]. P.P. Dong, Y.Y. Zhang, G.B. Ge, C.Z. Ai, Y. Liu, L. Yang, C.X. Liu, Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors, Acta Pharmacologica Sinica 29 (2008) 385-396. DOI: 10.1111/j.1745-7254.2008.00746.x. 10.1111/j.1745-7254.2008.00746.xOpen DOISearch in Google Scholar

[81]. H. Morita, A. Gonda, L. Wei, K. Takeya, H. Itokawa, 3D QSAR Analysis of Taxoids from Taxus Cuspidata Var. Nana by Comparative Molecular Field Approach, Bioorganic & Medicinal Chemistry Letters 7 (1997) 2387-2392. DOI: 10.1016/S0960-894X(97)00439-3 10.1016/S0960-894X(97)00439-3Open DOISearch in Google Scholar

[82]. N.C. Comelli, E.V. Ortiz, M. Kolacz, A.P. Toropova, A.A. Toropov, P.R. Duchowicz, E.A. Castro, Conformation-independent QSAR on c- Src tyrosine kinase inhibitors, Chemometrics and Intelligent Laboratory Systems 134 (2014) 47-52. DOI: 10.1016/j.chemolab.2014.03.003 10.1016/j.chemolab.2014.03.003Open DOISearch in Google Scholar

[83]. M.W.Jr. Chase, C.A. Davies, J.R.Jr. Downey, D.J. Frurip, R.A. McDonald, A.N. Syverud, JANAF Thermochemical Tables, Third Edition, Journal of Physical and Chemical Reference Data 14(1985) pp. 1856. Search in Google Scholar

[84]. S.D. Bolboacă, L. Jäntschi, Comparison of Quantitative Structure-Activity Relationship Model Performances on Carboquinone Derivatives, The Scientific World Journal 9 (2009) 1148-1166. DOI: 10.1100/tsw.2009.131 10.1100/tsw.2009.131582313019838601Open DOISearch in Google Scholar

[85]. K. Roy, Chapter 7 - Validation of QSAR Models. In: Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, AcademicPres, pp. 231-289 (2015). Search in Google Scholar

[86]. L. Jia, Z. Shen, W. Guo, Y. Zhang, H. Zhu, W. Jia, M. Fan, QSAR models for oxidative degradation of organic pollutants in the Fenton process, Journal of the Taiwan Institute of Chemical Engineers 46 (2015) 140-147. DOI: 10.1016/j.jtice.2014.09.014 10.1016/j.jtice.2014.09.014Open DOISearch in Google Scholar

[87]. H. Zhu, W. Guo, Z. Shen, Q. Tang, W. Ji, L. Jia, QSAR models for degradation of organic pollutants in ozonation process under acidic condition, Chemosphere 119 (2015) 65-71. DOI: 10.1016/j.chemosphere.2014.05.068 10.1016/j.chemosphere.2014.05.068Open DOISearch in Google Scholar

[88]. S. Cassani, S. Kovarich, E. Papa, P.P. Roy, L. van der Wal, P. Gramatica, Daphnia and fish toxicity of (benzo)triazoles: Validated QSAR models, and interspecies quantitative activity-activity modeling, Journal of Hazardous Materials 258-259 (2013) 50-60. DOI: 10.1016/j.jhazmat.2013.04.025 10.1016/j.jhazmat.2013.04.025Open DOISearch in Google Scholar

[89]. N.C. Comelli, P.R. Duchowicz, E.A. Castro, QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1, European Journal of Pharmaceutical Sciences 62 (2014) 171-179. DOI: 10.1016/j.ejps.2014.05.029 10.1016/j.ejps.2014.05.029Open DOISearch in Google Scholar

[90]. D. Verma, P. Kumar, B. Narasimhan, K. Ramasamy, V. Mani, R.K. Mishra, A.B.A. Majeed, Synthesis, antimicrobial, anticancer and QSAR studies of 1-[4-(substituted phenyl)-2-(substituted phenyl azomethyl)-benzo[b]-[1,4]diazepin-1-yl]-2-substituted phenylaminoethanones, Arabian Journal of Chemistry (2015) DOI: 10.1016/j.arabjc.2015.06.010 10.1016/j.arabjc.2015.06.010Open DOISearch in Google Scholar

[91]. M.D. Vitorović-Todorović, I.N. Cvijetić, I.O. Juranić, B.J. Drakulić, The 3D-QSAR study of 110 diverse, dual binding, acetylcholinesterase inhibitors based on alignment independent descriptors (GRIND-2). The effects of conformation on predictive power and interpretability of the models, Journal of Molecular Graphics and Modelling 38 (2012) 194-210. DOI: 10.1016/j.jmgm.2012.08.001 10.1016/j.jmgm.2012.08.001Open DOISearch in Google Scholar

[92]. T. Tunç, Y. Koç, L. Açık, M.S. Karacan, N. Karacan, DNA cleavage, antimicrobial studies and a DFT-based QSAR study of new antimony(III) complexes as glutathione reductase inhibitor, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 136 (2015) 1418-1427. DOI: 10.1016/j.saa.2014.10.030 10.1016/j.saa.2014.10.030Open DOISearch in Google Scholar

[93]. X. Hui-Ying, Z. Jian-Wei, H. Gui-Xiang, W. Wei, QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs), Chemosphere 80 (2010) 665-670. 10.1016/j.chemosphere.2010.04.050Search in Google Scholar

[94]. R. Miri, K. Javidnia, H. Mirkhani, B. Hemmateenejad, Z. Sepeher, M. Zalpour, T. Behzad, M. Khoshneviszadeh, N. Edraki, A.R. Mehdipour, Synthesis, QSAR and Calcium Channel Modulator Activity of New Hexahydroquinoline Derivatives Containing Nitroimidazole, Chemical Biology & Drug Design 70 (2007) 329-336. DOI: 10.1111/j.1747-0285.2007.00565.x 10.1111/j.1747-0285.2007.00565.xOpen DOISearch in Google Scholar

[95]. M.H. Abraham, R. Kumarsingh, J.E. Cometto-Muniz, W.S. Cain, A Quantitative Structure±Activity Relationship (QSAR) for a Draize Eye Irritation Database, Toxicology in Vitro 12 (1998) 201-207. DOI: 10.1016/S0887-2333(97)00117-3 10.1016/S0887-2333(97)00117-3Open DOISearch in Google Scholar

[96]. S.D. Bolboacă, L. Jäntschi, From molecular structure to molecular design through the Molecular Descriptors Family Methodology, In: Castro, E.A. (Ed.), QSPR-QSAR Studies on Desired Properties for Drug Design. Research Signpost, Transworld Research Network, pp. 117-166 (2010). Search in Google Scholar

[97]. L. Jäntschi, S.D. Bolboacă, M.V. Diudea, Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization, International Journal of Molecular Sciences 8 (2007) 1125-1157. Search in Google Scholar

[98]. L. Quesada-Romero, K. Mena-Ulecia, W. Tiznado, J. Caballero, Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR, PLoS ONE 9 (2014) e102212. DOI: 10.1371/journal.pone.0102212 10.1371/journal.pone.0102212409212625010341Search in Google Scholar

[99]. C. Zhao, Y. Zhang, P. Zou, J. Wang, W. He, D. Shi, H. Li, G. Liang, S. Yang, Synthesis and biological evaluation of a novel class of curcumin analogs as anti-inflammatory agents for prevention and treatment of sepsis in mouse model, Drug Design, Development and Therapy 9 (2015) 1663-1678. DOI: 10.2147/DDDT.S75862 10.2147/DDDT.S75862437091725834403Open DOISearch in Google Scholar

[100].S.J. Hocart, H. Liu, H. Deng, D. De, F.M. Krogstad, D.J. Krogstad, 4-Aminoquinolines Active against Chloroquine-Resistant Plasmodium falciparum: Basis of Antiparasite Activity and Quantitative Structure-Activity Relationship Analyses, Antimicrobial Agents and Chemotherapy 55 (2011) 2233-2244. DOI: 10.1128/AAC.00675-10 10.1128/AAC.00675-10308822421383099Open DOISearch in Google Scholar

[101].K.E. Hevener, D.M. Ball, J.K. Buolamwini, R.E. Lee, Quantitative structure-activity relationship studies on nitrofuranyl antitubercular agents, Bioorganic & Medicinal Chemistry 16 (2008) 8042-8053. DOI: 10.1016/j.bmc.2008.07.070 10.1016/j.bmc.2008.07.070259699218701298Open DOISearch in Google Scholar

[102].R.L. Dykstra, On dependent tests of significance in the multivariate analysis of variance, The Annals of Statistics 7 (1979) 459-461. 10.1214/aos/1176344628Open DOISearch in Google Scholar

[103].R.A. Fisher, Statistical Methods for Research Workers, Oliver and Boyd, Edinburgh, Scotland (1932). Search in Google Scholar

[104].E.S. Pearson, The Probability Integral Transformation for Testing Goodness of Fir and Combining Independent Tests of Significance, Biometrika 30 (1938) 134-148. DOI: 10.2307/2332229 10.2307/2332229Open DOISearch in Google Scholar

[105].W.A. Wallie, Compounding Probabilities from Independent Significance Tests, Econometrica 10 (1942) 229-248. DOI: 10.2307/1905466 10.2307/1905466Search in Google Scholar

[106].A. Birnbaum, Combining Independent Tests of Significance, Journal of the American Statistical Association 49 (1954) 559-574. DOI: 10.2307/2281130 10.2307/2281130Open DOISearch in Google Scholar

[107].S.A. Stouffer, E.A. Suchman, L.C. De Vinney, S.A. Star, R.M.Jr. Williams, The American Soldier: Adjustment during army life, Princeton University Press, Princeton, New York (1949). Search in Google Scholar

[108].F. Mosteller, R.R. Bush, Selected quantitative techniques, In: G. Lindzey, (Ed.) Handbook of Social Psychology, Addison-Wesley, Cambridge, 1 (1954) 289-334. Search in Google Scholar

[109].T. Liptak, On the combination of independent tests, Magyar Tudományos Akadémia Matematikai Kutató Intézete 3 (1958) 171-197. Search in Google Scholar

[110].Madhusudan Bhandary, Xuan Zhang, Comparison of Several Tests for Combining Several Independent Tests, Journal of Modern and Applied Statistical Methods 10 (2011) 436-444. 10.22237/jmasm/1320120240Search in Google Scholar

[111].E. Levonian, An Alternative to the Fisher and Pearson Methods for Combining Tests of Significance, Perceptual and Motor Skills 61 (1985) 967-983. 10.2466/pms.1985.61.3.967Search in Google Scholar

[112].M.C. Whitlock, Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach, Journal of Evolutionary Biology 18 (2005) 1368-1373. DOI: 10.1111/j.1420-9101.2005.00917.x 10.1111/j.1420-9101.2005.00917.x16135132Open DOISearch in Google Scholar

eISSN:
2286-038X
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Chemistry, other