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The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta-analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta-analysis on the partial correlation between self-confidence and sports performance.

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster., Peer Reviewed

Introduction: It is important to determine the accuracy of measurements relative to potential treatment effects, with time intervals between tests. Purpose: The aim of this study was to assess the error of measurement for blood parameters, physiological, and performance measures after the decay of short-term heat acclimation. Methods: Ten trained males (Mean±SD: age 28±7 y; body mass 74.6±4.4 kg; 4.26±0.37 L.min-1; peak power output (PPO) 329±42 W) completed an exercising heat stress test (HST) at baseline, 2nd day after acclimation and then during decay at 1, 2, 3 and 5-6 wks. CoV (95% CI), SE (95% CI) and Pearsons (r) were used for analysis of blood volume (blood, plasma, red cell volume, mean hemoglogin mass); plasma (aldosterone, arginine vasopressin [AVP], total protein, albumin, sodium); physiological (rectal temperature, cardiac frequency) and performance (exercise performance capacity, PPO). Results: The CoV (95% CI), SE (95% CI) and r with a 1-wk interval for blood volume was 2.3% (1.6 to 4.3; 1.9 [1.3 to 3.4 mL.Kg-1]; r=0.93; n=10). After 2-wk and 5-6 wks this had increased to 4.9% (3.4 to 9.3; 3.8 [2.6 to 7.0 mL.Kg-1]; r=0.76; n=9) and 5.5% (3.6 to 12.8; 4.5 [2.9 to 10.0 mL.Kg-1]; r=0.65; n=7) respectively. Conclusions: Blood volume and physiological measures demonstrated the least error one week apart but increased thereafter. Plasma concentrations and performance markers had the greatest error with repeat measures after one week. Therefore, for greater reliability and low measurement error measures should be taken no more than one week a part in repeated experimentation.

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design (p×i×r and p× [i:r]). A bootstrap technique called parametric methods with spherical random effects consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate.

The purpose of this study is to determine MOHV21, a Moho depth model based on an optimal combination of five global seismic and gravimetric-isostatic models of Moho depth by a weighted least squares approach at a resolution of 1° × 1°. For proper weighting among the data, the study starts with determining (mostly missing) standard errors and correlations among the models. The standard errors among the input models range from 1.0 (in Brazil) to 6.8 km (in Peru) and from 0.1 (in Huna Bay) to 6.0 km (in East Pacific Ridge) for Moho depth on land and ocean, respectively. The correlations among the five models range between − 0.99 and + 0.90. The Moho depths for MOHV21 at land regions vary between 14.5 (at the Horn of Africa) and 75 km (in the Himalayas) and between 6.6 (in the Greenland Sea) and 51.8 (in the Gulf of Bothnia) for land and ocean regions, respectively (However, note that, the Gulf of Bothnia belongs to continental crust, while the oceanic crust is generally within 20 km). The standard errors are generally within a few km but reaches 6.8 km (9%) in the highest mountains. The shallow Moho depths along mid-ocean ridges are well exposed in the model. Notable regional Moho highs are visualized in the Tarim basin in NW China of 59 ± 6.5 km and in Central Finland of 57 ± 4.7 km. A comparison of MOHV21 with a mosaic of regional models shows large differences reaching ± 25 km in Africa, Antarctic, and parts of S. America, while the differences are relatively modest in those parts of oceans that are available in the regional models., QC 20230322

Introduction: It is important to determine the accuracy of measurements relative to potential treatment effects, with time intervals between tests. Purpose: The aim of this study was to assess the error of measurement for blood parameters, physiological, and performance measures after the decay of short-term heat acclimation. Methods: Ten trained males (Mean±SD: age 28±7 y; body mass 74.6±4.4 kg; 4.26±0.37 L.min-1; peak power output (PPO) 329±42 W) completed an exercising heat stress test (HST) at baseline, 2nd day after acclimation and then during decay at 1, 2, 3 and 5-6 wks. CoV (95% CI), SE (95% CI) and Pearsons (r) were used for analysis of blood volume (blood, plasma, red cell volume, mean hemoglogin mass); plasma (aldosterone, arginine vasopressin [AVP], total protein, albumin, sodium); physiological (rectal temperature, cardiac frequency) and performance (exercise performance capacity, PPO). Results: The CoV (95% CI), SE (95% CI) and r with a 1-wk interval for blood volume was 2.3% (1.6 to 4.3; 1.9 [1.3 to 3.4 mL.Kg-1]; r=0.93; n=10). After 2-wk and 5-6 wks this had increased to 4.9% (3.4 to 9.3; 3.8 [2.6 to 7.0 mL.Kg-1]; r=0.76; n=9) and 5.5% (3.6 to 12.8; 4.5 [2.9 to 10.0 mL.Kg-1]; r=0.65; n=7) respectively. Conclusions: Blood volume and physiological measures demonstrated the least error one week apart but increased thereafter. Plasma concentrations and performance markers had the greatest error with repeat measures after one week. Therefore, for greater reliability and low measurement error measures should be taken no more than one week a part in repeated experimentation.

Introduction: It is important to determine the accuracy of measurements relative to potential treatment effects, with time intervals between tests. Purpose: The aim of this study was to assess the error of measurement for blood parameters, physiological, and performance measures after the decay of short-term heat acclimation. Methods: Ten trained males (Mean±SD: age 28±7 y; body mass 74.6±4.4 kg; 4.26±0.37 L.min-1; peak power output (PPO) 329±42 W) completed an exercising heat stress test (HST) at baseline, 2nd day after acclimation and then during decay at 1, 2, 3 and 5-6 wks. CoV (95% CI), SE (95% CI) and Pearsons (r) were used for analysis of blood volume (blood, plasma, red cell volume, mean hemoglogin mass); plasma (aldosterone, arginine vasopressin [AVP], total protein, albumin, sodium); physiological (rectal temperature, cardiac frequency) and performance (exercise performance capacity, PPO). Results: The CoV (95% CI), SE (95% CI) and r with a 1-wk interval for blood volume was 2.3% (1.6 to 4.3; 1.9 [1.3 to 3.4 mL.Kg-1]; r=0.93; n=10). After 2-wk and 5-6 wks this had increased to 4.9% (3.4 to 9.3; 3.8 [2.6 to 7.0 mL.Kg-1]; r=0.76; n=9) and 5.5% (3.6 to 12.8; 4.5 [2.9 to 10.0 mL.Kg-1]; r=0.65; n=7) respectively. Conclusions: Blood volume and physiological measures demonstrated the least error one week apart but increased thereafter. Plasma concentrations and performance markers had the greatest error with repeat measures after one week. Therefore, for greater reliability and low measurement error measures should be taken no more than one week a part in repeated experimentation.

The objective of this study was to evaluate the progress of heritabilities of the traits comprising the linear appraisal system in the Murciano-Granadina breed during the complete decade from December 2011 to December 2021. The estimated values for heritability were obtained from multivariate analyzes using the BLUP methodology and MTDFREML software. For 2021 heritabilities, a simple animal model was applied to records collected from 22,727 primiparous goats and 17,111 multiparous goats belonging to 85 herds. The model included the linear and quadratic and linear components of the covariates age and days in milk, respectively. The fixed effects considered in the model were herd, reproductive status, calving month and herd/year interaction. The animal was considered as a random effect. The variables studied included five characteristics related to Structure and capacity; Height at withers (EST), Body Depth (PC), Chest Width (AP), Rump Width (AG) and Rump Angle (ANG). Two traits related to dairy Structure; Angularity (ANGUL), Bone quality (HUESO). Six related to the mammary system; Anterior Insertion (IA), Rear Insertion Height (AIP), Median Suspensor Ligament (LSM), Udder Depth (PU), Nipple Placement (CP) and Nipple Diameter (DP). Finally, three related to legs and feet; Rear and lateral views of the rear legs (VPPT and VLPT) and Mobility (MOV). The heritabilities for structure and capacity characters progressed from 0.22 to 0.28 including non-convergent variables (PC and ANG) in June 2012 to values between 0.10 and 0.41 with all variables converging in June 2011. 2021. Heritabilities for dairy structure progressed from 0.18 with non-convergent variables (HUESO) in 2011 to 0.17 to 0.25 in 2021. Heritabilities for mammary system traits progressed from 0.12 to 0, 27 with non-convergent variables (AU) in 2012 to between 0.10 and 0.41 in 2021. For legs and feet, heritabilities progressed from 0.16 to 0.17 with non-convergent variables (VLPT) to 0.09 a 0.22. Genetic progr, El objetivo de este estudio fue evaluar el progreso de la heredabilidad para los caracteres considerados dentro del sistema de calificación lineal en la raza Murciano-Granadina durante la década completa desde diciembre de 2011 hasta diciembre de 2021. Los valores estimados para la heredabilidad fueron obtenidos a partir de análisis multivariados utilizando la metodología BLUP y el software MTDFREML. Para el cálculo de las heredabilidades de 2021, se aplicó un modelo animal simple sobre los registros recogidos en 22727 cabras primíparas y 17111 cabras multíparas, pertenecientes a 85 ganaderías. El modelo incluyó los componentes lineal y cuadrático y lineal de las covariables edad y días en leche, respectivamente. Los efectos fijos considerados en el modelo fueron el rebaño, status reproductivo, el mes de parto y la interacción rebaño/año. Por su parte se consideró el animal como efecto aleatorio. Las variables estudiadas incluyeron cinco características relacionadas con la Estructura y capacidad; Altura a la cruz (EST), Profundidad corporal (PC), Anchura de pecho (AP), Anchura de grupa (AG) y Ángulo de la Grupa (ANG). Dos rasgos relacionados con la Estructura lechera; Angulosidad (ANGUL), Calidad de hueso (HUESO). Seis relacionados con el Sistema mamario; Inserción anterior (IA), Altura inserción posterior (AIP), Ligamento suspensorio medio (LSM), Profundidad de la ubre (PU), Colocación de pezones (CP) and Diámetro de pezones (DP). Finalmente, tres relacionados con Patas y pies; Vistas posterior y lateral de las patas traseras (VPPT y VLPT) y Movilidad (MOV). Las heredabilidades para los caracteres de estructura y capacidad progresaron desde 0,22 a 0,28 incluyendo variables no convergentes (PC y ANG) en junio de 2011 a valores de entre 0,10 a 0,41 con todas las variables convergiendo en junio de 2021. Las heredabilidades para la estructura lechera progresaron desde 0,18 con variables no convergentes (HUESO) en 2012 hasta 0,17 a 0,25 en 2021. Las heredabilidades para

One of the most obvious features of time-to-event data is the occurrence of censoring. Rarely, if ever, studies are conducted until all participants experience the event of interest. Some participants survive beyond the end of follow-up time, some drop out from the studies for various non-study related reasons. During research planning it is paramount to consider the effect of censoring the follow-up times on the estimates. Herein, we look into the possibility of assessingthe loss of information, as measured by the variability of the survival probability estimates under right censoring. We provide the researchers with an easy to use formula to assess the magnitude of variance inflation due to censoring. Additionally, we conducted simulation studies assuming various survival distributions. We conclude that the provided variance inflation estimator can be an accurate practical tool for applied statisticians.

This paper introduces an alternative way of randomizing Sobol′ sequences, called the Column Shift method, for reconstructing replicates to improve estimation of the uncertainty in sensitivity indices. The Column Shift method provides reliable results when applied to variance-based sensitivity analysis of the V-function, with much higher accuracy than commonly used randomization methods in most circumstances. It also addresses the error spikes caused by determinism within the Sobol′ sequence. The Column Shift method is compared with other popular randomization methods for the Sobol′ sequence, and it is shown to be the most consistent of those tested. In addition, the inclusion of standard error in the mean of sensitivity indices in an analysis of replicates provides a good indication of underestimation of errors in simulation results. The relationship between the number of samples and replicates is also discussed.

The objective of this study was to evaluate the progress of heritabilities of the traits comprising the linear appraisal system in the Murciano-Granadina breed during the complete decade from December 2011 to December 2021. The estimated values for heritability were obtained from multivariate analyzes using the BLUP methodology and MTDFREML software. For 2021 heritabilities, a simple animal model was applied to records collected from 22,727 primiparous goats and 17,111 multiparous goats belonging to 85 herds. The model included the linear and quadratic and linear components of the covariates age and days in milk, respectively. The fixed effects considered in the model were herd, reproductive status, calving month and herd/year interaction. The animal was considered as a random effect. The variables studied included five characteristics related to Structure and capacity; Height at withers (EST), Body Depth (PC), Chest Width (AP), Rump Width (AG) and Rump Angle (ANG). Two traits related to dairy Structure; Angularity (ANGUL), Bone quality (HUESO). Six related to the mammary system; Anterior Insertion (IA), Rear Insertion Height (AIP), Median Suspensor Ligament (LSM), Udder Depth (PU), Nipple Placement (CP) and Nipple Diameter (DP). Finally, three related to legs and feet; Rear and lateral views of the rear legs (VPPT and VLPT) and Mobility (MOV). The heritabilities for structure and capacity characters progressed from 0.22 to 0.28 including non-convergent variables (PC and ANG) in June 2012 to values between 0.10 and 0.41 with all variables converging in June 2011. 2021. Heritabilities for dairy structure progressed from 0.18 with non-convergent variables (HUESO) in 2011 to 0.17 to 0.25 in 2021. Heritabilities for mammary system traits progressed from 0.12 to 0, 27 with non-convergent variables (AU) in 2012 to between 0.10 and 0.41 in 2021. For legs and feet, heritabilities progressed from 0.16 to 0.17 with non-convergent variables (VLPT) to 0.09 a 0.22. Genetic progr, El objetivo de este estudio fue evaluar el progreso de la heredabilidad para los caracteres considerados dentro del sistema de calificación lineal en la raza Murciano-Granadina durante la década completa desde diciembre de 2011 hasta diciembre de 2021. Los valores estimados para la heredabilidad fueron obtenidos a partir de análisis multivariados utilizando la metodología BLUP y el software MTDFREML. Para el cálculo de las heredabilidades de 2021, se aplicó un modelo animal simple sobre los registros recogidos en 22727 cabras primíparas y 17111 cabras multíparas, pertenecientes a 85 ganaderías. El modelo incluyó los componentes lineal y cuadrático y lineal de las covariables edad y días en leche, respectivamente. Los efectos fijos considerados en el modelo fueron el rebaño, status reproductivo, el mes de parto y la interacción rebaño/año. Por su parte se consideró el animal como efecto aleatorio. Las variables estudiadas incluyeron cinco características relacionadas con la Estructura y capacidad; Altura a la cruz (EST), Profundidad corporal (PC), Anchura de pecho (AP), Anchura de grupa (AG) y Ángulo de la Grupa (ANG). Dos rasgos relacionados con la Estructura lechera; Angulosidad (ANGUL), Calidad de hueso (HUESO). Seis relacionados con el Sistema mamario; Inserción anterior (IA), Altura inserción posterior (AIP), Ligamento suspensorio medio (LSM), Profundidad de la ubre (PU), Colocación de pezones (CP) and Diámetro de pezones (DP). Finalmente, tres relacionados con Patas y pies; Vistas posterior y lateral de las patas traseras (VPPT y VLPT) y Movilidad (MOV). Las heredabilidades para los caracteres de estructura y capacidad progresaron desde 0,22 a 0,28 incluyendo variables no convergentes (PC y ANG) en junio de 2011 a valores de entre 0,10 a 0,41 con todas las variables convergiendo en junio de 2021. Las heredabilidades para la estructura lechera progresaron desde 0,18 con variables no convergentes (HUESO) en 2012 hasta 0,17 a 0,25 en 2021. Las heredabilidades para

Робота містить 111 сторінок, 35 рисунків та 27 таблиць. Було використано 23 джерела. Мета роботи: є підвищення ефективності процесу радіомоніторингу джерел телекомунікаційних мереж і систем за рахунок підвищення точності, швидкодії та достовірності визначення пеленгу джерела радіовипромінювань. Проаналізовано проблемні питання вимірювання відносного пеленгу джерел радіовипромінювання телекомунікаційних мереж і систем та основні напрями їх вирішення. Розроблена структурно-функціональна схема приймально-пеленгаторного модуля моноімпульсного радіомоніторингу телекомунікаційних мереж. Розроблена структурно-функціональна схема аналого-цифрового двоканального вимірювача відносного пеленгу джерел радіовипромінювання телекомунікаційних мереж, відповідна програма та алгоритм обчислення відносного пеленгу. Розраховано графічні залежності щодо точності вимірювання відносного пеленгу джерел радіовипромінювання телекомунікаційних мереж. Розроблений стартап-проект аналого-цифрового пристрою двоканального вимірювання відносного пеленгу джерел радіовипромінювання телекомунікаційних мереж., The work contains 111 pages, 35 figures and 27 tables. 23 sources have been used. Goal: to increase the efficiency of the process of radio monitoring of sources of telecommunications networks and systems by improving the accuracy, speed and reliability of determining the bearing of the source of radio emission. The problematic issues of measuring the relative bearing of radio radiation sources of telecommunication networks and systems and the main directions of their solution are analyzed. The structural-functional scheme of the receiving-direction-finding module of monopulse radio monitoring of telecommunication networks is developed. The structural-functional scheme of the analog-digital two-channel meter of relative bearing of radio emission sources of telecommunication networks, the corresponding program and algorithm of calculation of relative bearing are developed. Graphical dependences on the accuracy of measuring the relative bearing of radio radiation sources of telecommunication networks are calculated. The start-up project of the analog-digital device of two-channel measurement of a relative bearing of sources of radio emission of telecommunication networks is developed.

Principal components analysis (PCA) is a common method to summarize a larger set of correlated variables into a smaller and more easily interpretable axes of variation. However, the different components need to be distinct from each other to be interpretable otherwise they only represent random directions. This is a fundamental assumption of PCA and, thus, needs to be tested every time. Sample correlation matrices will always result in a pattern of decreasing eigenvalues even if there is no structure. Tests are, therefore, needed to discern real patterns from illusionary ones. Furthermore, the loadings of the vectors need to be larger than expected by random data to be useful in the calculation of PC-scores. PC-scores calculated from nondistinct PC's have very large standard errors and cannot be used for biological interpretations. I give a number of examples to illustrate the potential problems with PCA. Robustness of the PC's increases with increasing sample size but not with the number of traits. I review a few simple test statistics appropriate for testing PC's and use a real-world example to illustrate how this can be done using randomization tests. PCA can be very useful but great care is needed to avoid spurious results.

In this paper, performance of different indicators for the profiling of Ultra Wide Band (UWB) wireless propagation channel are analyzed. In particular, the γ-indicator and the kurtosis index k are compared in terms of the standard error. In order to improve the accuracy in the kurtosis case, results of the bootstrap error procedure are also accomplished. Further, an evaluation on the computational time needed for error estimation, is also provided. The comparison is made according to a real set of data derived from UWB measurement campaign accomplished within a modern laboratory/office building in which the two above mentioned indicators have been evaluated.

IntroductionThe Glasgow Coma Scale (GCS) score is the primary method of assessing consciousness after traumatic brain injury (TBI), and the clinical standard for classifying TBI severity. There is scant literature discerning the influence of circadian rhythms or emergency department (ED) arrival hour on this important clinical tool.MethodsRetrospective cohort analysis of adult patients suffering blunt TBI using the National Sample Program of the National Trauma Data Bank, years 2003-2006. ED arrival GCS score was characterized by midday (10 a.m.-4 p.m.) and midnight (12 a.m.-6 a.m.) cohorts (N=24548). Proportions and standard errors are reported for descriptive data. Multivariable regressions using odds ratios (OR), mean differences (B), and their associated 95% confidence intervals [CI] were performed to assess associations between ED arrival hour and GCS score. Statistical significance was assessed at p<0.05.ResultsPatients were 42.48±0.13-years-old and 69.5% male. GCS score was 12.68±0.13 (77.2% mild, 5.2% moderate, 17.6% severe-TBI). Overall, patients were injured primarily via motor vehicle accidents (52.2%) and falls (24.2%), and 85.7% were admitted to hospital (33.5% ICU). Injury severity score did not differ between day and nighttime admissions.Nighttime admissions associated with decreased systemic comorbidities (p<0.001) and increased likelihood of alcohol abuse and drug intoxication (p<0.001). GCS score demonstrated circadian rhythmicity with peak at 12 p.m. (13.03±0.08) and nadir at 4am (12.12±0.12). Midnight patients demonstrated lower GCS (12 a.m.-6 a.m.: 12.23±0.04; 10 a.m.-4 p.m.: 12.95±0.03, p<0.001). Multivariable regression adjusted for demographic and injury factors confirmed that midnight-hours independently associated with decreased GCS (B=-0.29 [-0.40, -0.19]).In patients who did not die in ED or go directly to surgery (N=21862), midnight-hours (multivariable OR 1.73 [1.30-2.31]) associa

In practice, models almost always have misfit. The misfit of a structural equation model (SEM) can affect the estimation of model parameters in terms of point estimates, standard errors (SEs), and confidence intervals (CIs). In this article, the performance of different methods are compared including: the delta method, the nonparametric bootstrap (NP-B) method, and the semi-parametric bootstrap (SP-B) method in estimating standardized model parameters under the influence of model misfit. Two methods are used to construct the CI of the bootstrap method, the standard percentile method and the bias-corrected and accelerated (BCa) method. A simulation study is conducted using an SEM model with different amounts of model misfit and various sample sizes. The results show that if the model is correctly specified, all methods give correct point estimates, SE estimates, and CI coverage rates. However, as the amount of model misfit increases, the estimates based on NP-B remain accurate and consistent, whose based on the delta method and SP-B deteriorateKeywords: misspecification, standard error, SEM, standardized parameters

IntroductionThe Glasgow Coma Scale (GCS) score is the primary method of assessing consciousness after traumatic brain injury (TBI), and the clinical standard for classifying TBI severity. There is scant literature discerning the influence of circadian rhythms or emergency department (ED) arrival hour on this important clinical tool.MethodsRetrospective cohort analysis of adult patients suffering blunt TBI using the National Sample Program of the National Trauma Data Bank, years 2003-2006. ED arrival GCS score was characterized by midday (10 a.m.-4 p.m.) and midnight (12 a.m.-6 a.m.) cohorts (N=24548). Proportions and standard errors are reported for descriptive data. Multivariable regressions using odds ratios (OR), mean differences (B), and their associated 95% confidence intervals [CI] were performed to assess associations between ED arrival hour and GCS score. Statistical significance was assessed at p<0.05.ResultsPatients were 42.48±0.13-years-old and 69.5% male. GCS score was 12.68±0.13 (77.2% mild, 5.2% moderate, 17.6% severe-TBI). Overall, patients were injured primarily via motor vehicle accidents (52.2%) and falls (24.2%), and 85.7% were admitted to hospital (33.5% ICU). Injury severity score did not differ between day and nighttime admissions.Nighttime admissions associated with decreased systemic comorbidities (p<0.001) and increased likelihood of alcohol abuse and drug intoxication (p<0.001). GCS score demonstrated circadian rhythmicity with peak at 12 p.m. (13.03±0.08) and nadir at 4am (12.12±0.12). Midnight patients demonstrated lower GCS (12 a.m.-6 a.m.: 12.23±0.04; 10 a.m.-4 p.m.: 12.95±0.03, p<0.001). Multivariable regression adjusted for demographic and injury factors confirmed that midnight-hours independently associated with decreased GCS (B=-0.29 [-0.40, -0.19]).In patients who did not die in ED or go directly to surgery (N=21862), midnight-hours (multivariable OR 1.73 [1.30-2.31]) associa

We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analyzing such heterogeneous data. By allowing different covariates to be relevant for model- 15 ing conditional quantile functions at different quantile levels, it permits a more realistic sparsity assumption and provides a more complete picture of the conditional distribution of a response variable. Existing work on penalized quantile regression has been mostly focused on point estimation. It is challenging to estimate the standard error. Although bootstrap procedures have recently been demonstrated effective for making inference for penalized mean regression, they 20 are not directly applicable to penalized quantile regression with heteroscedastic errors.We prove that a wild residual bootstrap procedure recently proposed by Feng et al. (2011) for unpenalized quantile regression is asymptotically valid for approximating the distribution of a penalized quantile regression estimator with an adaptive L1 penalty; and that a modified version of this wild residual bootstrap procedure can be used to approximate the distribution of L1 penalized 25 quantile regression. We establish the bootstrap consistency theory, demonstrate appropriate finite sample performance through a simulation study, and illustrate its application using an ozone effects data set. The new methods do not need to estimate the unknown error density function.

In practice, models almost always have misfit. The misfit of a structural equation model (SEM) can affect the estimation of model parameters in terms of point estimates, standard errors (SEs), and confidence intervals (CIs). In this article, the performance of different methods are compared including: the delta method, the nonparametric bootstrap (NP-B) method, and the semi-parametric bootstrap (SP-B) method in estimating standardized model parameters under the influence of model misfit. Two methods are used to construct the CI of the bootstrap method, the standard percentile method and the bias-corrected and accelerated (BCa) method. A simulation study is conducted using an SEM model with different amounts of model misfit and various sample sizes. The results show that if the model is correctly specified, all methods give correct point estimates, SE estimates, and CI coverage rates. However, as the amount of model misfit increases, the estimates based on NP-B remain accurate and consistent, whose based on the delta method and SP-B deteriorateKeywords: misspecification, standard error, SEM, standardized parameters

IntroductionThe Glasgow Coma Scale (GCS) score is the primary method of assessing consciousness after traumatic brain injury (TBI), and the clinical standard for classifying TBI severity. There is scant literature discerning the influence of circadian rhythms or emergency department (ED) arrival hour on this important clinical tool.MethodsRetrospective cohort analysis of adult patients suffering blunt TBI using the National Sample Program of the National Trauma Data Bank, years 2003-2006. ED arrival GCS score was characterized by midday (10 a.m.-4 p.m.) and midnight (12 a.m.-6 a.m.) cohorts (N=24548). Proportions and standard errors are reported for descriptive data. Multivariable regressions using odds ratios (OR), mean differences (B), and their associated 95% confidence intervals [CI] were performed to assess associations between ED arrival hour and GCS score. Statistical significance was assessed at p<0.05.ResultsPatients were 42.48±0.13-years-old and 69.5% male. GCS score was 12.68±0.13 (77.2% mild, 5.2% moderate, 17.6% severe-TBI). Overall, patients were injured primarily via motor vehicle accidents (52.2%) and falls (24.2%), and 85.7% were admitted to hospital (33.5% ICU). Injury severity score did not differ between day and nighttime admissions.Nighttime admissions associated with decreased systemic comorbidities (p<0.001) and increased likelihood of alcohol abuse and drug intoxication (p<0.001). GCS score demonstrated circadian rhythmicity with peak at 12 p.m. (13.03±0.08) and nadir at 4am (12.12±0.12). Midnight patients demonstrated lower GCS (12 a.m.-6 a.m.: 12.23±0.04; 10 a.m.-4 p.m.: 12.95±0.03, p<0.001). Multivariable regression adjusted for demographic and injury factors confirmed that midnight-hours independently associated with decreased GCS (B=-0.29 [-0.40, -0.19]).In patients who did not die in ED or go directly to surgery (N=21862), midnight-hours (multivariable OR 1.73 [1.30-2.31]) associa

Background: Assessing library diversity is an important control step in a directed evolution experiment. To do this, a limited amount of colonies from a test library are sequenced and tested. In the case of an error-prone PCR library, the spectrum of the identified mutations - the proportions of mutations of a specific nucleobase to another-is calculated enabling the user to make more informed predictions on library diversity and coverage. However, the calculations of the mutational spectrum are severely affected by the limited sample sizes.Results: Here an online program, called Mutanalyst, is presented, which not only automates the calculations, but also estimates errors involved. Specifically, the errors are calculated thanks to the complementarity of DNA, which means that a mutation has a complementary mutation on the other sequence. Additionally, in the case of determining the mean number of mutations per sequence it does so by fitting to a Poisson distribution, which is more robust than calculating the average in light of the small sampling size.Conclusion: As a result of the added measures to keep into account of small sample size the user can better assess whether the library is satisfactory or whether error-prone PCR conditions should be adjusted. The program is available at www.mutanalyst.com.

The focus of this dissertation is on extending targeted learning to settings with complex unknown dependence structure, with an emphasis on applications in environmental science and environmental health.The bulk of the work in targeted learning and semiparametric inference in general has been with respect to data generated by independent units. Truly independent, randomized experiments in the environmental sciences and environmental health are rare, and data indexed by time and/or space is quite common. These scientific disciplines need flexible algorithms for model selection and model combining that can accommodate things like physical process models and Bayesian hierarchical approaches. They also need inference that honestly and realistically handles limited knowledge about dependence in the data.The goal of the research program reflected in this dissertation is to formalize results and build tools to address these needs. Chapter 1 provides a brief introduction to the context and spirit of the work contained in this dissertation.Chapter 2 focuses on Super Learner for spatial prediction. Spatial prediction is an important problem in many scientific disciplines, and plays an especially important role in the environmental sciences. We review the optimality properties of Super Learner in general and discuss the assumptions required in order for them to hold when using Super Learner for spatial prediction. We present results of a simulation study confirming Super Learner works well in practice under a variety of sample sizes, sampling designs, and data-generating functions. We also apply Super Learner to a real world, benchmark dataset for spatial prediction methods.Appendix A contains a theorem extending an existing oracle inequality to the case of fixed design regression.Chapter 3 describes a new approach to standard error estimation called Sieve Plateau (SP) variance estimation, an approach that allows us to learn from sequences of influence function based variance

The focus of this dissertation is on extending targeted learning to settings with complex unknown dependence structure, with an emphasis on applications in environmental science and environmental health.The bulk of the work in targeted learning and semiparametric inference in general has been with respect to data generated by independent units. Truly independent, randomized experiments in the environmental sciences and environmental health are rare, and data indexed by time and/or space is quite common. These scientific disciplines need flexible algorithms for model selection and model combining that can accommodate things like physical process models and Bayesian hierarchical approaches. They also need inference that honestly and realistically handles limited knowledge about dependence in the data.The goal of the research program reflected in this dissertation is to formalize results and build tools to address these needs. Chapter 1 provides a brief introduction to the context and spirit of the work contained in this dissertation.Chapter 2 focuses on Super Learner for spatial prediction. Spatial prediction is an important problem in many scientific disciplines, and plays an especially important role in the environmental sciences. We review the optimality properties of Super Learner in general and discuss the assumptions required in order for them to hold when using Super Learner for spatial prediction. We present results of a simulation study confirming Super Learner works well in practice under a variety of sample sizes, sampling designs, and data-generating functions. We also apply Super Learner to a real world, benchmark dataset for spatial prediction methods.Appendix A contains a theorem extending an existing oracle inequality to the case of fixed design regression.Chapter 3 describes a new approach to standard error estimation called Sieve Plateau (SP) variance estimation, an approach that allows us to learn from sequences of influence function based variance

Using acoustic locators is effective to solve the problems of studying wind conditions in the atmospheric boundary layer. These locators are based on the monostatic and bistatic principle. In the first case, the wind velocity vector is determined by the results of sounding in three directions, in the second - three-channel system with three different receiving antennas, radiation patterns of which intersect vertically-oriented radiation pattern of the transmitter antenna at a given point in space is used. In each direction of signal reception by the measured value of the Doppler frequency shift, the value of the wind velocity projection at the geometric axis of the receiving antenna is determined and then using the appropriate matrix transformations, wind velocity projections in the Cartesian coordinate system are calculated. The most important characteristic of any measurement system is the total standard measurement error, the value of which depends on the system configuration and total error components, defined by technical characteristics of the system. The analysis of the dependence of the measurement error of the most important for the practice horizontal component of the wind velocity depending on the antenna elevation angle was carried out, the nonlinear dependence of this quantity was shown. Its values are minimized in the certain range of angles that are optimal for the system construction., Для измерения скорости ветра в атмосферном пограничном слое эффективным является применение акустических локаторов. Представлена методика определения оптимальных углов места антенн в системах акустического зондирования атмосферного пограничного слоя, что обеспечивает минимизацию среднеквадратических погрешностей измерения горизонтальной составляющей скорости ветра. Приведены полученные расчетные формулы, определен диапазон значений углов места направлений зондирования, обеспечивающих минимальные ошибки., Для вимірювання швидкості вітру в атмосферному прикордонному шарі ефективним є застосування акустичних локаторів. Подано методику визначення оптимальних кутів місця антен в системах акустичного зондування атмосферного прикордонного шару, що забезпечує мінімізацію середньоквадратичних похибок вимірювання горизонтальної складової швидкості вітру. Наведені отримані розрахункові формули, визначений діапазон значень кутів місця напрямків зондування, забезпечуючих мінімальні похибки.

Normal-distribution-based maximum likelihood (ML) and multiple imputation (MI) are the two major procedures for missing data analysis. This article compares the two procedures with respects to bias and efficiency of parameter estimates. It also compares formula-based standard errors (SEs) for each procedure against the corresponding empirical SEs. The results indicate that parameter estimates by MI tend to be less efficient than those by ML; and the estimates of variance -covariance parameters by MI are also more biased. In particular, when the population for the observed variables possesses heavy tails, estimates of variance -covariance parameters by MI may contain severe bias even at relative large sample sizes. Although performing a lot better, ML parameter estimates may also contain substantial bias at smaller sample sizes. The results also indicate that, when the underlying population is close to normally distributed, SEs based on the sandwich-type covariance matrix and those based on the observed information matrix are very comparable to empirical SEs with either ML or MI. When the underlying distribution has heavier tails, SEs based on the sandwich-type covariance matrix for ML estimates are more reliable than those based on the observed information matrix. Both empirical results and analysis show that neither SEs based on the observed information matrix nor those based on the sandwich-type covariance matrix can provide consistent SEs in MI. Thus, ML is preferable to MI in practice, although parameter estimates by MI might still be consistent.

This study compared the performance of a local and three robust optimality criteria in terms of the standard error for a one-parameter and a two-parameter nonlinear model with uncertainty in the parameter values. The designs were also compared in conditions where there was misspecification in the prior parameter distribution. The impact of different correlation between parameters on the optimal design was examined in the two-parameter model. The designs and standard errors were solved analytically whenever possible and numerically otherwise.

In applied geostatistics, the semivariogram is commonly estimated from experimental data, producing an empirical semivariogram for a specified number of discrete lags. In a second stage, a model defined by a few parameters is fitted to the empirical semivariogram. As the experimental data are usually few and sparsely located, there is considerable uncertainty about the calculated semivariogram values (uncertainty of the empirical semivariogram) and about the parameters of any model fitted to them (uncertainty of the estimated model parameters). In this paper, the uncertainty in the modeling of the empirical semivariogram is numerically assessed by the generalized bootstrap, which is an extension of the classic bootstrap procedure modified for spatially correlated data. A computer program is described and provided for the assessment of those uncertainties. In particular, the program provides for the empirical semivariogram: the standard errors, the bootstrap percentile confidence intervals, the complete variance–covariance matrix, standard deviation correlation matrix. A public domain, natural dataset is used to illustrate the performance of the program. A promising result is that, for any distance, the median of the bootstrap distribution for the empirical semivariogram approximates more closely the underlying semivariogram than the estimate derived from the empirical sample

In applied geostatistics, the semivariogram is commonly estimated from experimental data, producing an empirical semivariogram for a specified number of discrete lags. In a second stage, a model defined by a few parameters is fitted to the empirical semivariogram. As the experimental data are usually few and sparsely located, there is considerable uncertainty about the calculated semivariogram values (uncertainty of the empirical semivariogram) and about the parameters of any model fitted to them (uncertainty of the estimated model parameters). In this paper, the uncertainty in the modeling of the empirical semivariogram is numerically assessed by the generalized bootstrap, which is an extension of the classic bootstrap procedure modified for spatially correlated data. A computer program is described and provided for the assessment of those uncertainties. In particular, the program provides for the empirical semivariogram: the standard errors, the bootstrap percentile confidence intervals, the complete variance–covariance matrix, standard deviation correlation matrix. A public domain, natural dataset is used to illustrate the performance of the program. A promising result is that, for any distance, the median of the bootstrap distribution for the empirical semivariogram approximates more closely the underlying semivariogram than the estimate derived from the empirical sample

We consider quantile regression models and investigate the induced smoothing method for obtaining the covariance matrix of the regression parameter estimates. We show that the difference between the smoothed and unsmoothed estimating functions in quantile regression is negligible. The detailed and simple computational algorithms for calculating the asymptotic covariance are provided. Intensive simulation studies indicate that the proposed method performs very well. We also illustrate the algorithm by analyzing the rainfall–runoff data from Murray Upland, Australia.

This thesis describes the design, maintenance and statistical analysis involved in undertaking a Longitudinal Survey. A longitudinal survey (or study) obtains observations or responses from individuals over several times over a defined period. This enables the direct study of changes in an individual's response over time. In particular, it distinguishes an individual's change over time from the baseline differences among individuals within the initial panel (or cohort). This is not possible in a cross-sectional study. As such, longitudinal surveys give correlated responses within individuals. Longitudinal studies therefore require different considerations for sample design and selection and analysis from standard cross-sectional studies. This thesis looks at the methodology for analysing social surveys. Most social surveys comprise of variables described as categorical variables. This thesis outlines the process of sample design and selection, interviewing and analysis for a longitudinal study. Emphasis is given to categorical response data typical of a survey. Included in this thesis are examples relating to the Goodna Longitudinal Survey and the Longitudinal Survey of Immigrants to Australia (LSIA). Analysis in this thesis also utilises data collected from these surveys. The Goodna Longitudinal Survey was conducted by the Queensland Office of Economic and Statistical Research (a portfolio office within Queensland Treasury) and began in 2002. It ran for two years whereby two waves of responses were collected.

The multiple-trait derivative-free REML set of programs was written to handle partially missing data for multiple-trait analyses as well as single- trait models. Standard errors of genetic parameters were reported for univariate models and for multiple-trait analyses only when all traits were measured on animals with records. In addition to estimating (co)variance components for multiple-trait models with partially missing data, this paper shows how the multiple-trait derivative-free REML set of programs can also estimate SE by augmenting the data file when not all animals have all traits measured. Although the standard practice has been to eliminate records with partially missing data, that practice uses only a subset of the available data. In some situations, the elimination of partial records can result in elimination of all the records, such as one trait measured in one environment and a second trait measured in a different environment. An alternative approach requiring minor modifications of the original data and model was developed that provides estimates of the SE using an augmented data set that gives the same residual log likelihood as the original data for multiple-trait analyses when not all traits are measured. Because the same residual vector is used for the original data and the augmented data, the resulting REML estimators along with their sampling properties are identical for the original and augmented data, so that SE for estimates of genetic parameters can be calculated.

A 'pseudo-Bayesian' interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes asymptotically a one-step procedure. This property also extends to covariance matrix calculation for rank estimates in multi-parameter problems. Examples, and some simple explanations, are given.

This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forecasting methods is discussed at the end of the chapter. The problem of error propagation, and hence inaccuracies, in the results of forecasting methods is briefly introduced to emphasize their main significance as tools in support of reaching organizational goals.

This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forecasting methods is discussed at the end of the chapter. The problem of error propagation, and hence inaccuracies, in the results of forecasting methods is briefly introduced to emphasize their main significance as tools in support of reaching organizational goals.

Dit onderzoek is gericht op de afstemming van vraag en aanbod van oppervlaktedelfstoffen. De afstemmingsproblematiek wordt geanalyseerd vanuit een perspectief van planning en besluitvorming. Doel van deze studie is het verkrijgen van nieuwe inzichten om de planning van ontgrondingen op rijksniveau beter te laten verlopen en te onderbouwen. Daarbij wordt primair gekeken naar de verticale coordinatie en afstemming van de planningsproblematiek tussen het rijk en de provincies. Hierbij komen zowel kwantitatieve als kwalitatieve aspecten aan bod.De aandacht gaat vooral uit naar oppervlaktedelfstoffen met een zogenaamde regionale schaarste. Regionale schaarste wordt omschreven als het relatieve gebrek aan een oppervlaktedelfstof in een bepaald gebied, in het licht van de op basis van de eigen behoefte van een gebied gegenereerde vraag naar deze delfstof. ...Zie: Samenvatting

Dit onderzoek is gericht op de afstemming van vraag en aanbod van oppervlaktedelfstoffen. De afstemmingsproblematiek wordt geanalyseerd vanuit een perspectief van planning en besluitvorming. Doel van deze studie is het verkrijgen van nieuwe inzichten om de planning van ontgrondingen op rijksniveau beter te laten verlopen en te onderbouwen. Daarbij wordt primair gekeken naar de verticale coordinatie en afstemming van de planningsproblematiek tussen het rijk en de provincies. Hierbij komen zowel kwantitatieve als kwalitatieve aspecten aan bod.De aandacht gaat vooral uit naar oppervlaktedelfstoffen met een zogenaamde regionale schaarste. Regionale schaarste wordt omschreven als het relatieve gebrek aan een oppervlaktedelfstof in een bepaald gebied, in het licht van de op basis van de eigen behoefte van een gebied gegenereerde vraag naar deze delfstof. ...Zie: Samenvatting

In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six "real world" medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number" evaluation of machine learning algorithms.

Ten behoeve van het Nederlandse ontgrondingenbeleid is er inzicht gewenst in toekomstige behoefte aan oppervlaktedelfstoffen. Hiervoor worden veelal modellen gebruikt. Over het voor beton- en metselzand gebruikte model waren enige twijfels gerezen. Daarom wordt dit model in deze studie tegen het licht gehouden. Het rapport bevat een beschrijving van het huidige model en vroegere modellen. Al deze modellen blijken te kort te schieten. Daarom worden alternatieve wijzen van modellering beschreven. Tevens wordt een materiaalstromenmodel opgesteld dat wordt gebruikt als het kader voor het nieuwe model en voor de data-analyse. Het rapport bevat een uitgebreide analyse van de beschikbare data. De verschillende tijdreeksen voor productie, gebruik, import en export van beton- en metselzand en industriezand worden beschreven. Verder worden diverse mogelijke verklarende variabelen besproken, zoals de cijfers voor de bouwproductie en de bouwinvesteringen. Vervolgens worden enkele mogelijke tijdreeksen d.m.v. lineaire regressie gerelateerd aan diverse verklarende variabelen. Op statistische gronden wordt één model voor het verbruik van beton- en metselzand geselecteerd. Met dit model worden vervolgens verkennende berekeningen gedaan voor de toekomstige behoefte aan beton- en metselzand.

This article examines the precision of measurements obtained from using the Rasch Dichotomous Model to analyse test data. Considering tests in which the item difficulties are uniformly spaced from easiest to most difficult, permits the derivation of an alternative expression for the standard error of measurement. This expression is sufficiently simple to enable the precision properties of uniform tests to be readily described and to enable a variety of problems of test construction to be solved. One particular problem is that of best test design. Regarding measurement precision as a property of the test only, we show that the best uniform test of a given length and a given target interval is the one that satisfies a minimax condition on the standard error. We illustrate the solution to this problem and describe properties of best tests.

This article examines the precision of measurements obtained from using the Rasch Dichotomous Model to analyse test data. Considering tests in which the item difficulties are uniformly spaced from easiest to most difficult, permits the derivation of an alternative expression for the standard error of measurement. This expression is sufficiently simple to enable the precision properties of uniform tests to be readily described and to enable a variety of problems of test construction to be solved. One particular problem is that of best test design. Regarding measurement precision as a property of the test only, we show that the best uniform test of a given length and a given target interval is the one that satisfies a minimax condition on the standard error. We illustrate the solution to this problem and describe properties of best tests.

This report details the processes and procedures used to transfer the Army Family Research Program (AFRP) Spouse Data Set, the Family Service Providers (SPF) Data Set, and the Installation and Community Characteristics Inventory (ICCI) Data Set from the National Institutes of Health mainframe computer to MS-DOS personal computer files for use by researchers at the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI). Procedures include a discussion on the appropriate job control language (JCL) to use with the Spouse file stored as a SAS data set, the macros used to generate the spouse data book, and the use of the Kermit communications package to download files to a PC. Included are also procedures to compute appropriate standard errors using the SUDAAN software. Special emphasis is placed on the description of procedures used to generate the various files. Standard error, SUDAAN, Family service providers, Spouse, AFRP, Army spouse characteristics. JCL, Army families.

Error estimates for Gaussian quadrature are given in terms of the number of quadrature points and smoothness properties of the function whose integral is being approximated. An intermediate step involves a weighted-L polynomial approximation problem which is treated in a more general context than that specifically required to estimate the Gaussian quadrature error., Sponsored in part by Grant NSF-MCS81-01631.

Certainly cogent arguments can be made for regressing the ability estimates obtained from unusual response patterns inward from their observed values. We have tried to elucidate some of them here. Yet experience has taught us that guessing is an activity that occurs frequently, and accounts for much (though by no means all) of the anomalous responses observed. Therefore we would like to propose a middle way. It would seem that a proper scheme to corrent for unusual response patterns would be in-between the traditional correction (that assumes that all anomalies are from guessing) and one that professes complete ignorance of the cause of such unusual response patterns. The method that we prefer regresses the 'corrected' ability estimates inward, although not usually as far as using an uncorrected raw score., This article is from Proceedings of the Annual Conference of the Military Testing Association (23rd) held at Arlington, Virginia on 25-30 October, 1981. Volume 2, AD-A130 703, p1335-1348.

The publication presents a current set of 1 degree x 1 degree mean free-air gravity anomalies, their respective estimated standard errors and the method used to compute/predict the magnitude of the anomalies.