Testing Means Using Hypothesis-Dependent Variance Estimates

The facial feedback hypothesis suggests that an individual’s experience of emotion is influenced by feedback from their facial movements. To evaluate the cumulative evidence for this hypothesis, we conducted a meta-analysis on 286 effect sizes derived from 138 studies that manipulated facial feedback and collected emotion self-reports. Using random effects meta-regression with robust variance estimates, we found that the overall effect of facial feedback was significant, but small. Results also indicated that feedback effects are stronger in some circumstances than others. We examined 12 potential moderators, and three were associated with differences in effect sizes. 1. Type of emotional outcome: Facial feedback influenced emotional experience (e.g., reported amusement) and, to a greater degree, affective judgments of a stimulus (e.g., the objective funniness of a cartoon). Three publication bias detection methods did not reveal evidence of publication bias in studies examining the effects of facial feedback on emotional experience, but all three methods revealed evidence of publication bias in studies examining affective judgments. 2. Presence of emotional stimuli: Facial feedback effects on emotional experience were larger in the absence of emotionally evocative stimuli (e.g., cartoons). 3. Type of stimuli: When participants were presented with emotionally evocative stimuli, facial feedback effects were larger in the presence of some types of stimuli (e.g., emotional sentences) than others (e.g., pictures). The available evidence supports the facial feedback hypothesis’ central claim that facial feedback influences emotional experience, although these effects tend to be small and heterogeneous.

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Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽

10.3390/ani11051445 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1445

Author(s):

Mauro Giammarino ◽

Silvana Mattiello ◽

Monica Battini ◽

Piero Quatto ◽

Luca Maria Battaglini ◽

...

Keyword(s):

Animal Welfare ◽

Confidence Intervals ◽

Bootstrap Method ◽

Dairy Goat ◽

Microsoft Excel ◽

Bootstrap Methods ◽

Welfare Indicators ◽

Observer Reliability ◽

High Concordance ◽

Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

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HEVC Fast Intra-Mode Selection Using World War II Technique

Electronics ◽

10.3390/electronics10090985 ◽

2021 ◽

Vol 10 (9) ◽

pp. 985

Author(s):

Junaid Tariq ◽

Ammar Armghan ◽

Fayadh Alenezi ◽

Amir Ijaz ◽

Saad Rehman ◽

...

Keyword(s):

World War Ii ◽

High Efficiency ◽

Mode Selection ◽

Estimation Algorithm ◽

Minimum Variance ◽

World War ◽

High Efficiency Video Coding ◽

Mode Estimation ◽

Compression Time ◽

Variance Estimates

High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%.

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Minimum variance estimates of gyroscopic drift parameters.

Journal of Spacecraft and Rockets ◽

10.2514/3.29350 ◽

1968 ◽

Vol 5 (6) ◽

pp. 747-749

Author(s):

CHARLES F. PRICE

Keyword(s):

Minimum Variance ◽

Variance Estimates

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Monte Carlo eigenvalue and variance estimates from several functional optimizations

The Journal of Chemical Physics ◽

10.1063/1.461532 ◽

1991 ◽

Vol 95 (9) ◽

pp. 6622-6633 ◽

Cited By ~ 48

Author(s):

S. A. Alexander ◽

R. L. Coldwell ◽

Hendrik J. Monkhorst ◽

J. D. Morgan

Keyword(s):

Monte Carlo ◽

Variance Estimates

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Variance additivity of genetic populational parameter estimates obtained through bootstrapping

Scientia Agricola ◽

10.1590/s0103-90162003000100015 ◽

2003 ◽

Vol 60 (1) ◽

pp. 97-103 ◽

Cited By ~ 1

Author(s):

Luciana Aparecida Carlini-Garcia ◽

Roland Vencovsky ◽

Alexandre Siqueira Guedes Coelho

Keyword(s):

Bootstrap Method ◽

Natural Populations ◽

Molecular Data ◽

Fixation Index ◽

Parameter Estimates ◽

Genetic Population ◽

Relative Contribution ◽

Population Structure Analysis ◽

Resampling Procedure ◽

Variance Estimates

Studying the genetic structure of natural populations is very important for conservation and use of the genetic variability available in nature. This research is related to genetic population structure analysis using real and simulated molecular data. To obtain variance estimates of pertinent parameters, the bootstrap resampling procedure was applied over different sampling units, namely: individuals within populations (I), populations (P), and individuals and populations simultaneously (I, P). The considered parameters were: the total fixation index (F or F IT), the fixation index within populations (f or F IS) and the divergence among populations or intrapopulation coancestry (theta or F ST). The aim of this research was to verify if the variance estimates of <IMG SRC="/img/fbpe/sa/v60n1/14549x09.gif">, <IMG SRC="/img/fbpe/sa/v60n1/14549x10.gif">and <IMG SRC="/img/fbpe/sa/v60n1/14549x11.gif">, found through the resampling over individuals and populations simultaneously (I, P), correspond to the sum of the respective variance estimates obtained from separated resampling over individuals and populations (I+P). This equivalence was verified in all cases, showing that the total variance estimate of <IMG SRC="/img/fbpe/sa/v60n1/14549x09.gif">, <IMG SRC="/img/fbpe/sa/v60n1/14549x10.gif">and <IMG SRC="/img/fbpe/sa/v60n1/14549x11.gif">can be obtained summing up the variances estimated for each source of variation separately. Results also showed that this facilitates the use of the bootstrap method on data with hierarchical structure and opens the possibility of obtaining the relative contribution of each source of variation to the total variation of estimated parameters.

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Estimating perimeter using graph cuts

Advances in Applied Probability ◽

10.1017/apr.2017.34 ◽

2017 ◽

Vol 49 (4) ◽

pp. 1067-1090 ◽

Cited By ~ 1

Author(s):

Nicolás García Trillos ◽

Dejan Slepčev ◽

James von Brecht

Keyword(s):

Confidence Intervals ◽

Error Estimates ◽

Approximation Error ◽

Higher Dimensions ◽

Geometric Graph ◽

Graph Cut ◽

Average Degree ◽

Random Geometric Graph ◽

Low Dimensions ◽

Variance Estimates

Abstract We investigate the estimation of the perimeter of a set by a graph cut of a random geometric graph. For Ω ⊆ D = (0, 1)d with d ≥ 2, we are given n random independent and identically distributed points on D whose membership in Ω is known. We consider the sample as a random geometric graph with connection distance ε > 0. We estimate the perimeter of Ω (relative to D) by the, appropriately rescaled, graph cut between the vertices in Ω and the vertices in D ∖ Ω. We obtain bias and variance estimates on the error, which are optimal in scaling with respect to n and ε. We consider two scaling regimes: the dense (when the average degree of the vertices goes to ∞) and the sparse one (when the degree goes to 0). In the dense regime, there is a crossover in the nature of the approximation at dimension d = 5: we show that in low dimensions d = 2, 3, 4 one can obtain confidence intervals for the approximation error, while in higher dimensions one can obtain only error estimates for testing the hypothesis that the perimeter is less than a given number.

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The Sleep Benefit in Episodic Memory: An Integrative Review and a Meta-Analysis

10.31234/osf.io/r2an6 ◽

2022 ◽

Author(s):

Sabrina Berres ◽

Edgar Erdfelder

Keyword(s):

Episodic Memory ◽

Meta Analysis ◽

Systematic Investigation ◽

Cued Recall ◽

Integrative Review ◽

Selective Reporting ◽

Potential Threat ◽

Sleep Study ◽

Sleep Benefit ◽

Variance Estimates

People recall more information after sleep than after an equally long period of wakefulness. This sleep benefit in episodic memory has been documented in almost a century of research. However, an integrative review of hypothesized underlying processes, a comprehensive quantification of the benefit, and a systematic investigation of potential moderators has been missing so far. Here, we address these issues by analyzing 823 effect sizes from 271 independent samples that were reported in 177 articles published between 1967 and 2019. Using multilevel meta-regressions with robust variance estimates, we found a moderate overall sleep benefit in episodic memory (g = 0.44). Moderator analyses revealed four important findings: First, the sleep benefit is larger when stimuli are studied multiple times instead of just once. Second, for word materials, the effect size depends on the retrieval procedure: It is largest in free recall, followed by cued recall and recognition tasks. Third, the sleep benefit is stronger in pre-post difference measures of retention than in delayed memory tests. Fourth, sleep benefits are larger for natural sleep and nighttime naps than foralternative sleep-study designs (e.g., SWS-deprived sleep, daytime naps). Although there was no obvious evidence for selective reporting, it is a potential threat to the validity of the results. When accounting for selective reporting bias, the overall effect of sleep on episodic memory is reduced but still significant (g = 0.28). We argue that our results support an integrative, multi-causal theoretical account of sleep-induced episodic memory benefits and provide guidance to increase their replicability.

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