scholarly journals A Statistical Estimation Approach for Quantitative Concentrations of Compounds Lacking Authentic Standards/Surrogates Based on Linear Correlations between Directly Measured Detector Responses and Carbon Number of Different Functional Groups

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yong-Hyun Kim ◽  
Ki-Hyun Kim
Keyword(s):  
Functional Groups ◽  
Statistical Estimation ◽  
Statistical Approach ◽  
Carbon Number ◽  
Predictive Equations ◽  
Experimental Bias ◽  
Volatile Organic ◽  
Practical Tool ◽  
The Mean ◽  
Linear Correlations

A statistical approach was investigated to estimate the concentration of compounds lacking authentic standards/surrogates (CLASS). As a means to assess the reliability of this approach, the response factor (RF) of CLASS is derived by predictive equations based on a linear regression (LR) analysis between the actual RF (by external calibration) of 18 reference volatile organic compounds (VOCs) consisting of six original functional groups and their physicochemical parameters ((1) carbon number (CN), (2) molecular weight (MW), and (3) boiling point (BP)). If the experimental bias is estimated in terms of percent difference (PD) between the actual and projected RF, the least bias for 18 VOCs is found from CN (17.9±19.0%). In contrast, the PD values against MW and BP are 40.6% and 81.5%, respectively. Predictive equations were hence derived via an LR analysis between the actual RF and CN for 29 groups: (1) one group consisting of all 18 reference VOCs, (2) three out of six original functional groups, and (3) 25 groups formed randomly from the six functional groups. The applicability of this method was tested by fitting these 29 equations into each of the six original functional groups. According to this approach, the mean PD for 18 compounds dropped as low as5.60±5.63%. This approach can thus be used as a practical tool to assess the quantitative data for CLASS.

scholarly journals A Functional Group Oxidation Model (FGOM) for SOA formation and aging

2012 ◽  
Vol 12 (12) ◽  
pp. 32565-32611 ◽  
Author(s):  
X. Zhang ◽  
J. H. Seinfeld
Keyword(s):  
Functional Groups ◽  
Functional Group ◽  
Carbon Number ◽  
Organic Aerosol ◽  
Relative Importance ◽  
Particle Phase ◽  
Carbon Backbone ◽  
Volatile Organic ◽  
Phase Oxidation ◽  
Oxidation Model

Abstract. Secondary organic aerosol (SOA) formation from a volatile organic compound (VOC) involves multiple generations of oxidation that include functionalization and fragmentation of the parent carbon backbone and, likely, particle-phase oxidation and/or accretion reactions. Despite the typical complexity of the detailed molecular mechanism of SOA formation and aging, a relatively small number of functional groups characterize the oxidized molecules that constitute SOA. Given the carbon number and set of functional groups, the volatility of the molecule can be estimated. We present here a Functional Group Oxidation Model (FGOM) that represents the process of SOA formation and aging. The FGOM contains a set of parameters that are to be determined by fitting of the model to laboratory chamber data: total organic aerosol concentration, and O:C and H:C atomic ratios. The sensitivity of the model prediction to variation of the adjustable parameters allows one to assess the relative importance of various pathways involved in SOA formation. An analysis of SOA formation from the high- and low-NOx photooxidation of four C12 alkanes (n-dodecane, 2-methylundecane, hexylcyclohexane, and cyclododecane) using the FGOM is presented, and comparison with the Statistical Oxidation Model (SOM) of Cappa et al. (2012) is discussed.

scholarly journals A functional group oxidation model (FGOM) for SOA formation and aging

2013 ◽  
Vol 13 (12) ◽  
pp. 5907-5926 ◽  
Author(s):  
X. Zhang ◽  
J. H. Seinfeld
Keyword(s):  
Functional Groups ◽  
Functional Group ◽  
Carbon Number ◽  
Organic Aerosol ◽  
Relative Importance ◽  
Particle Phase ◽  
Carbon Backbone ◽  
Volatile Organic ◽  
Phase Oxidation ◽  
Oxidation Model

Abstract. Secondary organic aerosol (SOA) formation from a volatile organic compound (VOC) involves multiple generations of oxidation that include functionalization and fragmentation of the parent carbon backbone and likely particle-phase oxidation and/or accretion reactions. Despite the typical complexity of the detailed molecular mechanism of SOA formation and aging, a relatively small number of functional groups characterize the oxidized molecules that constitute SOA. Given the carbon number and set of functional groups, the volatility of the molecule can be estimated. We present here a functional group oxidation model (FGOM) that represents the process of SOA formation and aging. The FGOM contains a set of parameters that are to be determined by fitting of the model to laboratory chamber data: total organic aerosol concentration, and O : C and H : C atomic ratios. The sensitivity of the model prediction to variation of the adjustable parameters allows one to assess the relative importance of various pathways involved in SOA formation. An analysis of SOA formation from the high- and low-NOx photooxidation of four C12 alkanes (n-dodecane, 2-methylundecane, hexylcyclohexane, and cyclododecane) using the FGOM is presented, and comparison with the statistical oxidation model (SOM) of Cappa et al. (2013) is discussed.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals Discussion of Identification, Estimation and Approximation of Risk under Interventions that Depend on the Natural Value of Treatment Using Observational Data, by Jessica Young, Miguel Hernán, and James Robins

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Mark J. van der Laan ◽  
Alexander R. Luedtke ◽  
Iván Díaz
Keyword(s):  
Statistical Model ◽  
Observational Data ◽  
Robust Estimation ◽  
Statistical Estimation ◽  
Estimation Problem ◽  
Target Parameter ◽  
Adaptive Parameters ◽  
The Mean ◽  
Data Adaptive ◽  
Statistical Estimation Problem

AbstractYoung, Hernán, and Robins consider the mean outcome under a dynamic intervention that may rely on the natural value of treatment. They first identify this value with a statistical target parameter, and then show that this statistical target parameter can also be identified with a causal parameter which gives the mean outcome under a stochastic intervention. The authors then describe estimation strategies for these quantities. Here we augment the authors’ insightful discussion by sharing our experiences in situations where two causal questions lead to the same statistical estimand, or the newer problem that arises in the study of data adaptive parameters, where two statistical estimands can lead to the same estimation problem. Given a statistical estimation problem, we encourage others to always use a robust estimation framework where the data generating distribution truly belongs to the statistical model. We close with a discussion of a framework which has these properties.

scholarly journals Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC<sup>4</sup>RS aircraft observations over the Southeast US

2016 ◽  
Author(s):  
Lei Zhu ◽  
Daniel J. Jacob ◽  
Patrick S. Kim ◽  
Jenny A. Fisher ◽  
Karen Yu ◽  
...  
Keyword(s):  
Transport Model ◽  
Research Groups ◽  
Chemical Transport Model ◽  
Shape Factors ◽  
Aircraft Observations ◽  
Volatile Organic ◽  
Satellite Retrievals ◽  
Mass Factor ◽  
The Mean ◽  
Southeast Us

Abstract. Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS campaign over the Southeast US in August–September 2013 to validate and intercompare six operational and research retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model provides a common intercomparison platform. We find that all retrievals capture the HCHO maximum over Arkansas and Louisiana, reflecting high emissions of biogenic isoprene, and are consistent in their spatial variability over the Southeast US (r = 0.4–0.8 on a 0.5° × 0.5° grid) as well as their day-to-day variability (r = 0.5–0.8). However, all satellite retrievals are biased low in the mean by 20–51 %, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has the highest corrected slant columns and the lowest scattering weights in its air mass factor (AMF) calculation. Correcting the assumed HCHO vertical profiles (shape factors) used in the AMF calculation would further reduce the bias in the OMI-BIRA data. We conclude that current satellite HCHO data provide a reliable proxy for isoprene emission variability but with a low mean bias due both to the corrected slant columns and the scattering weights used in the retrievals.

scholarly journals Hacer estimaciones estadísticas

2017 ◽  
Vol 5 (10) ◽  
Author(s):  
M. H Badii
Keyword(s):  
Confidence Intervals ◽  
Statistical Estimation ◽  
Good Estimator ◽  
The Mean ◽  
The Difference

Keywords: Estimations, sampling, statisticsAbstract. The notion of statistical estimation both in terms of point and interval is described. The criteria of a good estimator are noted. The procedures to calculate the intervals for the mean, proportions and the difference among two means as well as the confidence intervals for the probable errors in statistics are provided.Palabras clave: Estadística, estimación, muestreoResumen. En la presente investigación se describen la noción de la estimación estadística, tanto de tipo puntual con de forma de intervalo. Se presentan los criterios que debe reunir un estimador bueno. Se notan con ejemplos, la forma de calcular la estimación del intervalo para la media, la proporción y de la diferencia entre dos medias y los intervalos de confianza para los errores probables.

scholarly journals Quantum Calculations to Estimate the Heat of Hydrogenation Theoretically

2020 ◽  
Author(s):  
Ali Amir Khairbek
Keyword(s):  
Unsaturated Hydrocarbon ◽  
Experimental Results ◽  
Basis Set ◽  
Good Linear ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Complete Basis Set Limit ◽  
The Mean ◽  
Experimental Values ◽  
Linear Correlations

Standard enthalpies of hydrogenation of 29 unsaturated hydrocarbon compounds were calculated in the gas phase by CCSD(T) theory with complete basis set cc-pVXZ, where X = DZ, TZ, as well as by complete basis set limit extrapolation. Geometries of reactants and products were optimized at the M06-2X/6-31g(d) level. This M06-2X geometries were used in the CCSD(T)/cc-pVXZ//M06-2X/6-31g(d) and cc-pV(DT)Z extrapolation calculations. (MAD) the mean absolute deviations of the enthalpies of hydrogenation between the calculated and experimental results that range from 8.8 to 3.4 kJ mol−1 based on the Comparison between the calculation at CCSD(T) and experimental results. The MAD value has improved and decreased to 1.5 kJ mol−1 after using complete basis set limit extrapolation. The deviations of the experimental values are located inside the “chemical accuracy” (±1 kcal mol−1 ≈ ±4.2 kJ mol−1) as some results showed. A very good linear correlations between experimental and calculated enthalpies of hydro-genation have been obtained at CCSD(T)/cc-pVTZ//M06-2X/6-31g(d) level and CCSD(T)/cc-pV(DT)Z extrapolation levels (SD =2.11 and 2.12 kJ mol−1, respectively).

scholarly journals Are Predictive Energy Expenditure Equations Accurate in Cirrhosis?

Nutrients ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 334 ◽  
Author(s):  
Tannaz Eslamparast ◽  
Benjamin Vandermeer ◽  
Maitreyi Raman ◽  
Leah Gramlich ◽  
Vanessa Den Heyer ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
English Language ◽  
Resting Energy Expenditure ◽  
Pooled Analysis ◽  
Energy Requirements ◽  
Significant Heterogeneity ◽  
Predictive Equations ◽  
Language Studies ◽  
The Mean ◽  
Mean Differences

Malnutrition is associated with significant morbidity and mortality in cirrhosis. An accurate nutrition prescription is an essential component of care, often estimated using time-efficient predictive equations. Our aim was to compare resting energy expenditure (REE) estimated using predictive equations (predicted REE, pREE) versus REE measured using gold-standard, indirect calorimetry (IC) (measured REE, mREE). We included full-text English language studies in adults with cirrhosis comparing pREE versus mREE. The mean differences across studies were pooled with RevMan 5.3 software. A total of 17 studies (1883 patients) were analyzed. The pooled cohort was comprised of 65% men with a mean age of 53 ± 7 years. Only 45% of predictive equations estimated energy requirements to within 90–110% of mREE using IC. Eighty-three percent of predictive equations underestimated and 28% overestimated energy needs by ±10%. When pooled, the mean difference between the mREE and pREE was lowest for the Harris–Benedict equation, with an underestimation of 54 (95% CI: 30–137) kcal/d. The pooled analysis was associated with significant heterogeneity (I2 = 94%). In conclusion, predictive equations calculating REE have limited accuracy in patients with cirrhosis, most commonly underestimating energy requirements and are associated with wide variations in individual comparative data.

An Adaptive Statistically Based Controller for Through-Feed Centerless Grinding

2000 ◽  
Vol 123 (3) ◽  
pp. 380-386 ◽  
Author(s):  
Richard W. Cowan ◽  
Daniel J. Schertz ◽  
Thomas R. Kurfess
Keyword(s):  
Standard Deviation ◽  
Statistical Approach ◽  
High Volume ◽  
Manufacturing Processes ◽  
Control Methods ◽  
Statistical Control ◽  
Accuracy And Precision ◽  
Self Tuning ◽  
Centerless Grinding ◽  
The Mean

The purpose of this research is to develop a statistically based controller that is “self-tuning.” High volume manufacturing processes such as through-feed centerless grinding are best controlled with a statistical approach, but traditional methods of statistical control generally rely on fixed parameters that must be determined. These values must be precisely known and the true physical characteristics they model must remain constant throughout grinding, or traditional statistical control methods may break down. The mean and standard deviation of a process are measures of its accuracy and precision. The scheme developed here makes control decisions based on the real-time values of these quantities. This self-adjusting ability can compensate for changes in machine parameters as they occur.

Sign in / Sign up

Export Citation Format

Share Document