Estimation of Parameters of Pert Distribution by Using Method of Moments

The method of moments has been widely used for estimating the parameters of a distribution. Usually lower order moments are wont to find the parameter estimates as they're known to possess less sampling variability. The method of moments may be a technique for estimating the parameters of a statistical model. It works by finding values of the parameters that end in a match between the sample moments and therefore the population moments (as implied by the model). the Method of moment Estimator is used to find out Estimates the parameters of PERT Distribution. We also compare equispaced and unequispaced Optimally Constructed Grouped data by the method of an Asymptotically Relative Efficiency. We also computed Average Estimate (AE), Variance (VAR), Standard Deviation (STD), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Simulated Error (SE) and Relative Absolute Bias (RAB) for both the parameters under grouped sample supported 1000 simulations to assess the performance of the estimators. Keywords: Method of Moments, PERT Distribution, equispaced and unequipped Optimal Grouped sample

Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)

In order to correctly detect climate signals and discard possible instrumentation errors, establishing coherent data records has become increasingly relevant. However, since real measurements can be inhomogeneous, their use for assessing homogenization techniques is not directly possible, and the study of their performance must be done on homogeneous datasets subjected to controlled, artificial inhomogeneities. In this paper, considering two European temperature networks over the 1950–2005 period, up to 7 artificial breaks and an average of 107 missing data per station were introduced, in order to determine that mean square error, absolute bias and factor of exceedance can be meaningfully used to validate the best-performing homogenization technique. Three techniques were used, ACMANT and two versions of HOMER: the standard, automated setup mode and a manual setup. Results showed that the HOMER techniques performed better regarding the factor of exceedance, while ACMANT was best with regard to absolute error and root mean square error. Regardless of the technique used, it was also established that homogenization quality anti-correlated meaningfully to the number of breaks. On the other hand, as missing data are almost always replaced in the two HOMER techniques, only ACMANT performance is significantly, negatively affected by the amount of missing data.

Cross-Validation of a Multiplex LC-MS/MS Method for Assaying mAbs Plasma Levels in Patients with Cancer: A GPCO-UNICANCER Study

Background: Different liquid chromatography tandem mass spectrometry (LC–MS/MS) methods have been published for quantification of monoclonal antibodies (mAbs) in plasma but thus far none allowed the simultaneous quantification of several mAbs, including immune checkpoint inhibitors. We developed and validated an original multiplex LC–MS/MS method using a ready-to-use kit to simultaneously assay 7 mAbs (i.e., bevacizumab, cetuximab, ipilimumab, nivolumab, pembrolizumab, rituximab and trastuzumab) in plasma. This method was next cross-validated with respective reference methods (ELISA or LC–MS/MS). Methods: The mAbXmise kit was used for mAb extraction and full-length stable-isotope-labeled antibodies as internal standards. The LC–MS/MS method was fully validated following current EMA guidelines. Each cross validation between reference methods and ours included 16–28 plasma samples from cancer patients. Results: The method was linear from 2 to 100 µg/mL for all mAbs. Inter- and intra-assay precision was <14.6% and accuracy was 90.1–111.1%. The mean absolute bias of measured concentrations between multiplex and reference methods was 10.6% (range 3.0–19.9%). Conclusions: We developed and cross-validated a simple, accurate and precise method that allows the assay of up to 7 mAbs. Furthermore, the present method is the first to offer a simultaneous quantification of three immune checkpoint inhibitors likely to be associated in patients.

Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much?

Background: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. Methods: Using data from “Predictive Study of Coronary Heart Disease” study, this study examined the effectiveness of multiple imputation in data with 20% missing to 80% missing observations using absolute bias (|bias|) and Root Mean Square Error (RMSE) of MI measured under Missing Completely at Random (MCAR), Missing at Random (MAR), and Not Missing at Random (NMAR) assumptions. Results: The |bias| and RMSE of MI was much smaller than of the results of CCA under all missing mechanisms, especially with a high percentage of missing. In addition, the |bias| and RMSE of MI were consistent regardless of increasing imputation numbers from M=10 to M=50. Moreover, when comparing imputation mechanisms, MCMC method had universally smaller |bias| and RMSE than those of Regression method and Predictive Mean Matching method under all missing mechanisms. Conclusion: As missing percentages become higher, using MI is recommended, because MI produced less biased estimates under all missing mechanisms. However, when large proportions of data are missing, other things need to be considered such as the number of imputations, imputation mechanisms, and missing data mechanisms for proper imputation.

Comparison of Different Response Time Outlier Exclusion Methods: A Simulation Study

In response time (RT) research, RT outliers are typically excluded from statistical analysis to improve the signal-to-noise ratio. Nevertheless, there exist several methods for outlier exclusion. This poses the question, how these methods differ with respect to recovering the uncontaminated RT distribution. In the present simulation study, two RT distributions with a given population difference were simulated in each iteration. RTs were replaced by outliers following two different approaches. The first approach generated outliers at the tails of the distribution, the second one inserted outliers overlapping with the genuine RT distribution. We applied ten different outlier exclusion methods and tested, how many pairs of distributions significantly differed. Outlier exclusion methods were compared in terms of bias. Bias was defined as the deviation of the proportion of significant differences after outlier exclusion from the proportion of significant differences in the uncontaminated samples (before introducing outliers). Our results showed large differences in bias between the exclusion methods. Some methods showed a high rate of Type-I errors and should therefore clearly not be used. Overall, our results showed that applying an exclusion method based on z-scores / standard deviations introduced only small biases, while the absence of outlier exclusion showed the largest absolute bias.

Evaluation of TROPOMI/Sentinel-5 Precursor NO2 product against ground-based observations in Helsinki and first applications to Finnish society

&lt;p&gt;We evaluate the satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products against ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer during April&amp;#8211;September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10% and 0.12 &amp;#215; 10&lt;sup&gt;15&lt;/sup&gt; molec. cm&lt;sup&gt;-2&lt;/sup&gt; respectively.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5 &amp;#215; 7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement, but the overall bias remains the same (within the uncertainties).&lt;/p&gt;&lt;p&gt;In order to evaluate the capability of TROPOMI observations for monitoring urban air quality, we also analyse the consistency between satellite-based data and NO2 surface concentrations from the Kumpula air quality station in Helsinki. We find similar day-to-day variability between TROPOMI and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites).&lt;/p&gt;&lt;p&gt;Several applications have been already carried on to support informed decision making and Finnish society in general. We developed a simple web platform to inform environmental authorities at municipal level about the use of satellite observations for air quality monitoring. We assisted the Finnish authorities during the first period of the COVID-19 pandemic in assessing the effect of the lockdown on air quality. We supported the Finnish Ministry of Environment in compiling the periodic national air pollution assessment report to the EU. We participated in several international cooperation projects for assessing the major air pollution sources and the available air quality monitoring systems over several developing countries and for providing recommendations on strengthening air quality monitoring. We collaborated with the department of Social Science at the Univ. of Helsinki for the assessment of the environmental impacts of the energy and extracting sector in Yakutia (Russia).&lt;/p&gt;&lt;p&gt;Reference: Ialongo, I., Virta, H., Eskes, H., Hovila, J., and Douros, J.: Comparison of TROPOMI/Sentinel-5 Precursor NO&lt;sub&gt;2&lt;/sub&gt; observations with ground-based measurements in Helsinki, Atmos. Meas. Tech., 13, 205&amp;#8211;218, https://doi.org/10.5194/amt-13-205-2020, 2020.&lt;/p&gt;

ANALISIS SURVIVAL UNTUK PARAMETER SKALA DARI DISTRIBUSI WEIBULL MENGGUNAKAN MLE DAN METODE BAYESIAN

Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.

Development of an algorithm for optimization of continuous technological process of cultivation of microorganisms

When implementing the tasks of controlling technological processes, finding the optimal control actions, and creating control algorithms that implement the optimal modes of technological processes, it is necessary to present the criterion of optimality in the form of a goal function, the extremum of which best meets the purpose of this object and expressed as - Relevant technical and economic indicators. The criterion of optimality should be an integral indicator that reflects the main aspects of production. Profit is most often taken as such a criterion for typical microbiological industries - as the most generalized indicator, reflecting almost all aspects of the enterprise. Possible criteria of optimality are analyzed in the form of technical and economic indicators of the process of cultivation of microorganisms, the extremum of which best meets the objectives of production and reflects the main aspects of the functioning of the control object. The analysis of possible modes of microalgae cultivation has been carried out. Two optimization algorithms are substantiated. The first one is based on random search method with an absolute bias, an algorithm for optimizing the process of cultivating microorganisms with continuous regeneration of the flow in one cultivator. The second is an algorithm for determining the optimal residence time of chlorella particles in multistage cultivators, focused on the method of dynamic programming implemented in Wellman's recurrence relation. The developed algorithm for operational forecasting and automatic control of the chlorella cultivation process allows, under given production conditions and the composition of nutrients, to increase the productivity of technological equipment and improve the quality of the target product, as well as to prevent in advance various unforeseen and emergency production situations.

Diagnostik einer eisendefizitären Erythropoese mittels nicht-invasiver Messung von Zink-Protoporphyrin-IX bei chirurgischen Patienten

Hintergrund: Patienten, die präoperativ an einer eisendefizitären Erythropoese (IDE) oder Anämie leiden, haben unabhängig von anderen Erkrankungen ein erhöhtes Risiko für postoperative Morbidität. Ein Eisenmangel ist der häufigste Grund für eine Anämie und kann, wenn er frühzeitig diagnostiziert wird, effizient mittels Eisensubstitution behandelt werden. Zink-Protoporphyrin (ZnPP) ist im Vergleich zu klassischen Parametern wie Ferritin ein vielversprechender Parameter, um eine IDE zu diagnostizieren. Bisher wurde der Parameter im Blut gemessen. Nun soll geprüft werden, ob eine nicht-invasive Messung valide Ergebnisse liefert. Methoden: Von März 2017 bis April 2018 wurden am Universitätsklinikum Frankfurt Patienten, die für eine Operation mit einem erwarteten Blutverlust von >10% geplant waren, auf eine IDE untersucht. Die Messung von nicht-invasivem ZnPP (ZnPP-NI) wurde mit der ZnPP-Referenz-Messung des ZnPP/Häm-Verhältnisses mittels Hochleistungsflüssigchromatographie (ZnPP-HPLC) verglichen. Die analytische Performance beim Nachweis einer IDE wurde mit im Blut gemessenen klassischen Eisenstatusparameter (Ferritin, Transferrinsättigung [TSAT], löslicher Transferrinrezeptor [sTfR] und sTfR-Index [sTfR-F]) verglichen. Ergebnis: In dieser prospektiven Studie konnten 285 chirurgische Patienten präoperativ untersucht werden. Die Limits of Agreement zwischen ZnPP-NI und ZnPP-HPLC betrugen 20,3 μmol/mol Häm (95% -Konfidenzintervall 18,0-21,3; Akzeptanzkriterien 24,4 μmol/mol Häm; absolute Bias -0,3 μmol/mol Häm). Die analytische Performance zum Nachweis einer IDE der im Blut gemessenen Parameter war: ZnPP-HPLC (0,95), sTfR (0,90), sTfR-F (0,89), ZnPP-NI (0,88), TSAT (0,87) und Ferritin (0,65). Fazit: Beim Nachweis einer IDE ist ZnPP-NI besser geeignet als Ferritin und vergleichbar valide wie TSAT. Der Vergleich mit einem Multiparameter-Index-Test ergab, dass ZnPP-NI von ≤40 μmol/mol Häm den Ausschluss einer IDE ermöglicht und ein Wert von ≥65 μmol/mol Häm eine IDE wahrscheinlich macht. ZnPP-NI kann daher für eine schnelle Erstbewertung in der IDE-Diagnostik und im Anämie Management ohne Blutentnahme verwendet werden.

Space-Borne GNSS-R Ionospheric Delay Error Elimination by Optimal Spatial Filtering

Global Navigation Satellite System Reflectometry (GNSS-R) technology is a new and promising remote sensing technology, especially satellite-based GNSS-R remote sensing, which has broad application prospects. In this work, the ionospheric impacts on space-borne GNSS-R sea surface altimetry were investigated. An analysis of optimal values for spatial filtering to remove ionospheric delays in space-borne GNSS-R altimetry was conducted. Considering that there are few satellite-borne GNSS-R orbit observations to date, simulated high-resolution space-borne GNSS-R orbital data were used for a comprehensive global and applicable study. The curves of absolute bias in relation to the bilateral filtering points were verified to achieve the minimum absolute bias. The optimal filtering points were evaluated in both statistical probability density and quantile analysis to show the reliability of the selected values. The proposed studies are helpful and valuable for the future implementation of high-accuracy space-borne GNSS-R sea surface altimetry.