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This study compared four different statistical methods, involving six estimation procedures, for addressing censored left data in measuring temporal trends of eight different pyrethroids measured in sediment from a 10-year data set in a residential California stream (Pleasant Grove Creek). The statistical methods used were: the Kaplan–Meier (km) method; the robust regression on order statistics (ros using normal and log normal distributions rosln); the maximum likelihood estimation (mlen using normal and log normal distributions mleln); and a substitution method (sub) using ½ the detection limit. For five of the eight pyrethroids (bifenthrin, cyfluthrin, cypermethrin, lambda-cyhalothrin, and permethrin), the six statistical methods generally agree, with one exception, that the data set exhibit significant declining trends. In the case of bifenthrin, the slight disagreement among statistical methods only occurred for the mleln estimate that did not show a significant declining trend, whereas the other five methods did. For deltamethrin, esfenvalerate, and fenpropathrin, all six statistical methods were in agreement showing no significant trends. Possible reasons for declining sediment concentrations of pyrethroids in Pleasant Grove Creek are urban label changes effective in 2012–2015 that reduced residential use, variable annual rainfall, and more responsible homeowner use based on outreach/education programs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative machine learning approach called NESTORE, which analyses seismic clusters to forecast strong earthquakes of magnitudes similar or greater to those of the mainshock. The method analyzes the seismicity in the first hours/days after the mainshock and provides the probability of having a strong subsequent earthquake. The analysis is conducted at various stages of time to simulate the increase in knowledge over time. We address the main problem of statistics and machine learning when applied to spatiotemporal variation of seismicity: the small datasets available, on the order of tens or fewer instances, need a more accurate analysis with respect to the classical testing procedures, where hundreds or thousands of data are available. In addition, we develop a more robust NESTORE method based on a jackknife approach (rNESTORE), and we successfully apply it to California seismicity. [Display omitted] • We study the occurrence of large earthquake following the mainshock in California. • NESTORE machine learning approach estimates of the probability of occurrence. • It uses features based on the ongoing seismic activity after the mainshock. • The analysis shows a false alarm rate < 0.2 and a hit rate up to 0.80. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of determining non-parametric confidence intervals for quantiles when available data are in the form of k-records. Distribution-free confidence intervals as well as lower and upper confidence limits are derived for fixed quantiles of an arbitrary unknown distribution based on k-records of an independent and identically distributed sequence from that distribution. The construction of tolerance intervals and limits based on k-records is also discussed. An exact expression for the confidence coefficient of these intervals are derived. Some tables are also provided to assist in choosing the appropriate k-records for the construction of these confidence intervals and tolerance intervals. Some simulation results are presented to point out some of the features and properties of these intervals. Finally, the data, representing the records of the amount of annual rainfall in inches recorded at Los Angeles Civic Center, are used to illustrate all the results developed in this paper and also to demonstrate the improvements that they provide on those based on either the usual records or the current records. [ABSTRACT FROM AUTHOR]