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@Article{LopesCros:2017:IIOb,
               author = "Lopes, Carlos Eduardo Ferreira and Cross, N. J. G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Scottish 
                         Universities Physics Alliance (SUPA)}",
                title = "New insights into time series analysis: II-Non-correlated 
                         observations",
              journal = "Astronomy and Astrophysics",
                 year = "2017",
               volume = "604",
                pages = "A121",
                month = "Aug.",
             keywords = "methods: data analysis  methods: statistical  techniques: 
                         photometric  astronomical databases: miscellaneous  stars: 
                         variables: general  infrared: general.",
             abstract = "Statistical parameters are used to draw conclusions in a vast 
                         number of fields such as finance, weather, industrial, and 
                         science. These parameters are also used to identify variability 
                         patterns on photometric data to select non-stochastic variations 
                         that are indicative of astrophysical effects. New, more efficient, 
                         selection methods are mandatory to analyze the huge amount of 
                         astronomical data. Aims. We seek to improve the current methods 
                         used to select non-stochastic variations on non-correlated data. 
                         Methods. We used standard and new data-mining parameters to 
                         analyze non-correlated data to find the best way to discriminate 
                         between stochastic and non-stochastic variations. A new approach 
                         that includes a modified Strateva function was performed to select 
                         non-stochastic variations. Monte Carlo simulations and public 
                         time-domain data were used to estimate its accuracy and 
                         performance. Results. We introduce 16 modified statistical 
                         parameters covering different features of statistical distribution 
                         such as average, dispersion, and shape parameters. Many dispersion 
                         and shape parameters are unbound parameters, i.e. equations that 
                         do not require the calculation of average. Unbound parameters are 
                         computed with single loop and hence decreasing running time. 
                         Moreover, the majority of these parameters have lower errors than 
                         previous parameters, which is mainly observed for distributions 
                         with few measurements. A set of non-correlated variability 
                         indices, sample size corrections, and a new noise model along with 
                         tests of different apertures and cut-offs on the data (BAS 
                         approach) are introduced. The number of mis-selections are reduced 
                         by about 520% using a single waveband and 1200% combining all 
                         wavebands. On the other hand, the even-mean also improves the 
                         correlated indices introduced in Paper I. The mis-selection rate 
                         is reduced by about 18% if the even-mean is used instead of the 
                         mean to compute the correlated indices in the WFCAM database. 
                         Even-statistics allows us to improve the effectiveness of both 
                         correlated and non-correlated indices. Conclusions. The selection 
                         of non-stochastic variations is improved by non-correlated 
                         indices. The even-Averages provide a better estimation of mean and 
                         median for almost all statistical distributions analyzed. The 
                         correlated variability indices, which are proposed in the first 
                         paper of this series, are also improved if the even-mean is used. 
                         The even-parameters will also be useful for classifying light 
                         curves in the last step of this project. We consider that the 
                         first step of this project, where we set new techniques and 
                         methods that provide a huge improvement on the efficiency of 
                         selection of variable stars, is now complete. Many of these 
                         techniques may be useful for a large number of fields. Next, we 
                         will commence a new step of this project regarding the analysis of 
                         period search methods.",
                  doi = "10.1051/0004-6361/201630109",
                  url = "http://dx.doi.org/10.1051/0004-6361/201630109",
                 issn = "0004-6361 and 1432-0746",
             language = "en",
           targetfile = "lopes_new.pdf",
        urlaccessdate = "04 dez. 2020"
}


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