A Graphic Approach to Summarizing Forestry Data

1987 ◽  
Vol 63 (5) ◽  
pp. 347-350 ◽  
Author(s):  
Stephen J. Titus

Boxplots are a useful enhancement to the traditional summary statistics such as the mean and variance. Based on the median and other percentiles of the data distribution, they provide more information in a graphic format which is convenient for interpreting the nature of one or several data sets Use of boxplots is illustrated with three common types of forestry data: 1) tree diameter distributions, 2) tree volume function residuals, and 3) forest inventory summaries.

2020 ◽  
Author(s):  
Marie-Pier Bergeron-Boucher ◽  
Jesús-Adrian Alvarez ◽  
Ilya Kashnitsky ◽  
Virginia Zarulli ◽  
James W Vaupel

Differences in lifespan between populations, e.g. between females and males, are often measured by differences in summary statistics, such as life expectancy, which generally show an advantage of females over males across the whole age span. However, such statistics ignore the fact that two lifespan distributions are generally not mutually exclusive and that not all females outlive all males. Here we use a novel measure of inequality in lifespans: the outsurvival probability, which is interpreted as the probability of males to outlive females. The measure accounts for the similarities in lifespan between populations. It also considers the interaction between the mean and variance of two lifespan distributions and their combined effect on between-populations inequalities. Our results show that the probability of males outliving females varied between 25% and 50%, across 44 countries and regions since the middle of the 18th century. Thus, despite the usually male lower life expectancy and higher death rates at all ages, males have a substantial chance of outliving females. Our suggested approach is generalizable to any pair of populations.


Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 342-351 ◽  
Author(s):  
Ralph R. B. von Frese ◽  
Michael B. Jones ◽  
Jeong Woo Kim ◽  
Jeong‐Hee Kim

Recognizing correlations between data sets is the basis for rationalizing geophysical interpretation and theory. Procedures are presented that constitute an effective process for identifying correlative features between two or more digital data sets. The procedures include the development of normalization factors from the mean and variance properties of the data sets. Using these factors, the data sets may be transformed so that they have common amplitude ranges, means, and variances, thereby allowing a common graphical representation of the data sets that facilitates the visualization of feature correlations. Anomaly features that show direct, inverse, or no correlations between data sets may be separated by the application of correlation filters in the frequency domains of the data sets. The correlation filter passes or rejects wavenumbers between coregistered data sets based on the correlation coefficient between common wavenumbers as given by the cosine of their phase difference. Standardizing and summing the filtered outputs where directly correlative features have been enhanced yields local favorability indices that optimize the perception of these features. Differencing the standardized outputs where inversely correlative features have been enhanced, on the other hand, provides favorability indices that improve the perception of the inverse correlations. This study includes a generic example, as well as magnetic and gravity anomaly profile examples that illustrate the usefulness of these procedures for extracting correlative features between digital data sets.


2020 ◽  
Author(s):  
Faezeh Bayat ◽  
Maxwell Libbrecht

AbstractMotivationA sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 100 and 200 reads usually has a very different statistical importance from a difference between 1,100 and 1,200 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications—including imputation and segmentation and genome annotation (SAGA)—instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance.ResultsWe show here that existing transformations do not fully stabilize variance in genomic data sets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencingbased genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal data set and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean-variance relationship models, and will enable genomic signals to be easily understood by [email protected]://github.com/faezeh-bayat/Variance-stabilized-units-for-sequencing-based-genomic-signals.


i-Perception ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 204166951774729 ◽  
Author(s):  
Yi Yang ◽  
Midori Tokita ◽  
Akira Ishiguchi

A number of studies revealed that our visual system can extract different types of summary statistics, such as the mean and variance, from sets of items. Although the extraction of such summary statistics has been studied well in isolation, the relationship between these statistics remains unclear. In this study, we explored this issue using an individual differences approach. Observers viewed illustrations of strawberries and lollypops varying in size or orientation and performed four tasks in a within-subject design, namely mean and variance discrimination tasks with size and orientation domains. We found that the performances in the mean and variance discrimination tasks were not correlated with each other and demonstrated that extractions of the mean and variance are mediated by different representation mechanisms. In addition, we tested the relationship between performances in size and orientation domains for each summary statistic (i.e. mean and variance) and examined whether each summary statistic has distinct processes across perceptual domains. The results illustrated that statistical summary representations of size and orientation may share a common mechanism for representing the mean and possibly for representing variance. Introspections for each observer performing the tasks were also examined and discussed.


Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.


Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 568
Author(s):  
Sabine G. Gebhardt-Henrich ◽  
Ariane Stratmann ◽  
Marian Stamp Dawkins

Group level measures of welfare flocks have been criticized on the grounds that they give only average measures and overlook the welfare of individual animals. However, we here show that the group-level optical flow patterns made by broiler flocks can be used to deliver information not just about the flock averages but also about the proportion of individuals in different movement categories. Mean optical flow provides information about the average movement of the whole flock while the variance, skew and kurtosis quantify the variation between individuals. We correlated flock optical flow patterns with the behavior and welfare of a sample of 16 birds per flock in two runway tests and a water (latency-to-lie) test. In the runway tests, there was a positive correlation between the average time taken to complete the runway and the skew and kurtosis of optical flow on day 28 of flock life (on average slow individuals came from flocks with a high skew and kurtosis). In the water test, there was a positive correlation between the average length of time the birds remained standing and the mean and variance of flock optical flow (on average, the most mobile individuals came from flocks with the highest mean). Patterns at the flock level thus contain valuable information about the activity of different proportions of the individuals within a flock.


Genetics ◽  
1999 ◽  
Vol 153 (1) ◽  
pp. 497-506 ◽  
Author(s):  
Rasmus Nielsen ◽  
Daniel M Weinreich

Abstract McDonald/Kreitman tests performed on animal mtDNA consistently reveal significant deviations from strict neutrality in the direction of an excess number of polymorphic nonsynonymous sites, which is consistent with purifying selection acting on nonsynonymous sites. We show that under models of recurrent neutral and deleterious mutations, the mean age of segregating neutral mutations is greater than the mean age of segregating selected mutations, even in the absence of recombination. We develop a test of the hypothesis that the mean age of segregating synonymous mutations equals the mean age of segregating nonsynonymous mutations in a sample of DNA sequences. The power of this age-of-mutation test and the power of the McDonald/Kreitman test are explored by computer simulations. We apply the new test to 25 previously published mitochondrial data sets and find weak evidence for selection against nonsynonymous mutations.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 955
Author(s):  
Alamir Elsayed ◽  
Mohamed El-Beltagy ◽  
Amnah Al-Juhani ◽  
Shorooq Al-Qahtani

The point kinetic model is a system of differential equations that enables analysis of reactor dynamics without the need to solve coupled space-time system of partial differential equations (PDEs). The random variations, especially during the startup and shutdown, may become severe and hence should be accounted for in the reactor model. There are two well-known stochastic models for the point reactor that can be used to estimate the mean and variance of the neutron and precursor populations. In this paper, we reintroduce a new stochastic model for the point reactor, which we named the Langevin point kinetic model (LPK). The new LPK model combines the advantages, accuracy, and efficiency of the available models. The derivation of the LPK model is outlined in detail, and many test cases are analyzed to investigate the new model compared with the results in the literature.


2008 ◽  
Vol 54 (1) ◽  
pp. 31-35
Author(s):  
Thomas G. Matney ◽  
Emily B. Schultz

Abstract Many growth and yield models have used statistical probability distributions to estimate the diameter distribution of a stand at any age. Equations for approximating individual tree diameter growth and survival probabilities from dbh can be derived from these models. A general procedure for determining the functions is discussed and illustrated using a loblolly pine spacing study. The results from the spacing study show that it is possible to define tree diameter growth and survival probability functions from diameter distributions with an accuracy sufficient to obtain a link between the individual tree and diameter growth and yield models.


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