scholarly journals Fat mass estimation in neonates: anthropometric models compared with air displacement plethysmography

2018 ◽  
Vol 121 (3) ◽  
pp. 285-290 ◽  
Author(s):  
Jami L. Josefson ◽  
Michael Nodzenski ◽  
Octavious Talbot ◽  
Denise M. Scholtens ◽  
Patrick Catalano
Keyword(s):  
Fat Mass ◽  
Prediction Error ◽  
Mean Squared Error ◽  
Epidemiological Studies ◽  
Concordance Correlation ◽  
Air Displacement Plethysmography ◽  
Mass Estimation ◽  
Independent Validation ◽  
Squared Error ◽  
Mean Prediction Error

AbstractNewborn adiposity, a nutritional measure of the maternal–fetal intra-uterine environment, is representative of future metabolic health. An anthropometric model using weight, length and flank skinfold to estimate neonatal fat mass has been used in numerous epidemiological studies. Air displacement plethysmography (ADP), a non-invasive technology to measure body composition, is impractical for large epidemiological studies. The study objective was to determine the consistency of the original anthropometric fat mass estimation equation with ADP. Full-term neonates were studied at 12–72 h of life with weight, length, head circumference, flank skinfold thickness and ADP measurements. Statistical analyses evaluated three models to predict neonatal fat mass. Lin’s concordance correlation coefficient, mean prediction error and root mean squared error between the predicted and observed ADP fat mass values were used to evaluate the models, where ADP was considered the gold standard. A multi-ethnic cohort of 468 neonates were studied. Models (M) for predicting fat mass were developed using 349 neonates from site 1, then independently evaluated in 119 neonates from site 2. M0 was the original anthropometric model, M1 used the same variables as M0 but with updated parameters and M2 additionally included head circumference. In the independent validation cohort, Lin’s concordance correlation estimates demonstrated reasonable accuracy (model 0: 0·843, 1: 0·732, 2: 0·747). Mean prediction error and root mean squared error in the independent validation was much smaller for M0 compared with M1 and M2. The original anthropometric model to estimate neonatal fat mass is reasonable for predicting ADP, thus we advocate its continued use in epidemiological studies.

PREDICTION ERROR AS A CRITERION FOR OPERATOR LENGTH

Geophysics ◽  
1971 ◽  
Vol 36 (2) ◽  
pp. 261-265 ◽  
Author(s):  
James N. Galbraith
Keyword(s):  
Prediction Error ◽  
Mean Squared Error ◽  
A Priori ◽  
Nonincreasing Function ◽  
Final Value ◽  
Kolmogorov Spectrum ◽  
Squared Error ◽  
Levinson Algorithm ◽  
The Mean ◽  
Error Filtering

Prediction error filtering has been widely used for deconvolution. The mean squared error in prediction is a monotonically nonincreasing function of operator length, and the value of the error is readily available from the Wiener‐Levinson algorithm. In general, the value of this error for the infinitely long operator is not known a priori. It is shown that the final value of the error can be obtained by considering the Kolmogorov spectrum factorization. Simple criteria can then be established for operator effectiveness and length.

scholarly journals Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models

2014 ◽  
Vol 26 (2) ◽  
pp. 796-808 ◽  
Author(s):  
Peter C Austin ◽  
Ewout W Steyerberg
Keyword(s):  
Logistic Regression ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Relative Performance ◽  
Validation Sample ◽  
Independent Validation ◽  
Squared Error ◽  
Split Sample ◽  
Performance Estimates

We conducted an extensive set of empirical analyses to examine the effect of the number of events per variable (EPV) on the relative performance of three different methods for assessing the predictive accuracy of a logistic regression model: apparent performance in the analysis sample, split-sample validation, and optimism correction using bootstrap methods. Using a single dataset of patients hospitalized with heart failure, we compared the estimates of discriminatory performance from these methods to those for a very large independent validation sample arising from the same population. As anticipated, the apparent performance was optimistically biased, with the degree of optimism diminishing as the number of events per variable increased. Differences between the bootstrap-corrected approach and the use of an independent validation sample were minimal once the number of events per variable was at least 20. Split-sample assessment resulted in too pessimistic and highly uncertain estimates of model performance. Apparent performance estimates had lower mean squared error compared to split-sample estimates, but the lowest mean squared error was obtained by bootstrap-corrected optimism estimates. For bias, variance, and mean squared error of the performance estimates, the penalty incurred by using split-sample validation was equivalent to reducing the sample size by a proportion equivalent to the proportion of the sample that was withheld for model validation. In conclusion, split-sample validation is inefficient and apparent performance is too optimistic for internal validation of regression-based prediction models. Modern validation methods, such as bootstrap-based optimism correction, are preferable. While these findings may be unsurprising to many statisticians, the results of the current study reinforce what should be considered good statistical practice in the development and validation of clinical prediction models.

scholarly journals An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity

2019 ◽  
pp. 1-9
Author(s):  
Lilia V Castro-Porras ◽  
Mario E Rojas-Russell ◽  
Javier Villanueva-Sánchez ◽  
Malaquías López-Cervantes
Keyword(s):  
Fat Mass ◽  
Epidemiological Studies ◽  
Cross Sectional Study ◽  
Fat Free Mass ◽  
Measurement Instruments ◽  
Mass Percentage ◽  
Cross Sectional ◽  
Air Displacement Plethysmography ◽  
Life Measurement ◽  
The Difference

AbstractObjectiveTo develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.DesignCross-sectional study.SettingMexican adults.ParticipantsAdults (n 275; 181 women) aged 20–63 years with BMI between 17·4 and 42·4 kg/m2.ResultsThirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.ConclusionsThe anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

Reliability Estimates for Regression Predictions

2011 ◽  
pp. 320-338 ◽  
Author(s):  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Prediction Error ◽  
Mean Squared Error ◽  
Noisy Data ◽  
Data Set ◽  
Squared Error ◽  
Reliability Estimates ◽  
Risk Sensitive ◽  
Average Accuracy ◽  
Applications Of Machine Learning

In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the atuhors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

Description and Prediction of Long-Term Learning of a Keyboarding Task

1992 ◽  
Vol 36 (4) ◽  
pp. 276-280 ◽  
Author(s):  
Mark McMulkin
Keyword(s):  
Prediction Error ◽  
Mean Squared Error ◽  
Acceptable Level ◽  
Learning Function ◽  
Squared Error ◽  
Data Points ◽  
Best Fitting ◽  
The Mean ◽  
Relationship Of

The goal of this study was to determine an equation (“learning function”) that describes long-term learning of a new keyboard. Five subjects learned 18 characters on a chord keyboard, then improved keying speed by inputting typical numeric keypad text for about 60 total hours. Their performance, in characters typed per minute, was recorded for every trial. Of the various functions that were considered to describe performance, the best fitting equation was a Log-Log relationship of the form CPMi = eb0Tib1, where CPMi is the performance in characters per minute on the i-th trial (Ti) and b0 and b1 are fitted coefficients. A second goal was to investigate how many trials of performance are needed before the entire learning function can be determined. The coefficients of the Log-Log function were determined using only the first 25, 50, 75, 100, 125, 150, 175, and 200 of the initial performance points (out of about 550 total actual data points). The mean squared error (MSE) was calculated for each of these fits and compared to the MSE of the fit using all points. From the results of MSE data, it appears that at least 50 performance data points are required to reduce the prediction error to an acceptable level.

scholarly journals Estimation of conditional mean squared error of prediction for claims reserving

2019 ◽  
Vol 14 (1) ◽  
pp. 93-128 ◽  
Author(s):  
Mathias Lindholm ◽  
Filip Lindskog ◽  
Felix Wahl
Keyword(s):  
Prediction Error ◽  
Linear Models ◽  
Mean Squared Error ◽  
Claim Amount ◽  
Conditional Mean ◽  
Squared Error ◽  
Run Off ◽  
Final Prediction Error ◽  
Wide Range ◽  
Out Of Sample

AbstractThis paper studies estimation of the conditional mean squared error of prediction, conditional on what is known at the time of prediction. The particular problem considered is the assessment of actuarial reserving methods given data in the form of run-off triangles (trapezoids), where the use of prediction assessment based on out-of-sample performance is not an option. The prediction assessment principle advocated here can be viewed as a generalisation of Akaike’s final prediction error. A direct application of this simple principle in the setting of a data-generating process given in terms of a sequence of general linear models yields an estimator of the conditional mean squared error of prediction that can be computed explicitly for a wide range of models within this model class. Mack’s distribution-free chain ladder model and the corresponding estimator of the prediction error for the ultimate claim amount are shown to be a special case. It is demonstrated that the prediction assessment principle easily applies to quite different data-generating processes and results in estimators that have been studied in the literature.

scholarly journals Estimation of percentage body fat in 6- to 13-year-old children by skinfold thickness, body mass index and waist circumference

2010 ◽  
Vol 104 (10) ◽  
pp. 1565-1572 ◽  
Author(s):  
Susi Kriemler ◽  
Jardena Puder ◽  
Lukas Zahner ◽  
Ralf Roth ◽  
Ursina Meyer ◽  
...  
Keyword(s):  
Waist Circumference ◽  
Body Fat ◽  
Mean Squared Error ◽  
Skinfold Thickness ◽  
Epidemiological Studies ◽  
Pubertal Stage ◽  
Percentage Body Fat ◽  
Squared Error ◽  
Selection Of ◽  
Better Than

We evaluated the accuracy of skinfold thicknesses, BMI and waist circumference for the prediction of percentage body fat (PBF) in a representative sample of 372 Swiss children aged 6–13 years. PBF was measured using dual-energy X-ray absorptiometry. On the basis of a preliminary bootstrap selection of predictors, seven regression models were evaluated. All models included sex, age and pubertal stage plus one of the following predictors: (1) log-transformed triceps skinfold (logTSF); (2) logTSF and waist circumference; (3) log-transformed sum of triceps and subscapular skinfolds (logSF2); (4) log-transformed sum of triceps, biceps, subscapular and supra-iliac skinfolds (logSF4); (5) BMI; (6) waist circumference; (7) BMI and waist circumference. The adjusted determination coefficient (R _{adj}^{2} ) and the root mean squared error (RMSE; kg) were calculated for each model. LogSF4 (R _{adj}^{2} 0·85; RMSE 2·35) and logSF2 (R _{adj}^{2} 0·82; RMSE 2·54) were similarly accurate at predicting PBF and superior to logTSF (R _{adj}^{2} 0·75; RMSE 3·02), logTSF combined with waist circumference (R _{adj}^{2} 0·78; RMSE 2·85), BMI (R _{adj}^{2} 0·62; RMSE 3·73), waist circumference (R _{adj}^{2} 0·58; RMSE 3·89), and BMI combined with waist circumference (R _{adj}^{2} 0·63; RMSE 3·66) (P < 0·001 for all values of R _{adj}^{2} ). The finding that logSF4 was only modestly superior to logSF2 and that logTSF was better than BMI and waist circumference at predicting PBF has important implications for paediatric epidemiological studies aimed at disentangling the effect of body fat on health outcomes.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

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