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PloS One 2020In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fréchet,...
In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fréchet, Lomax, Burr III, inverse gamma and generalized gamma distributions. For each of the proposed distribution, we give expressions for the probability density function, cumulative distribution function, moments and characteristic function. GARCH models with innovations taken to follow the compound distributions are fitted to the data using the method of maximum likelihood. For the sample data considered, we see that all but two of the proposed distributions perform better than two popular distributions. Finally, we perform a simulation study to examine the accuracy of the best performing model.
Topics: Computer Simulation; Financial Management; Humans; Investments; Likelihood Functions; Models, Economic; Models, Statistical; Statistical Distributions
PubMed: 33006975
DOI: 10.1371/journal.pone.0239652 -
Journal of the Mechanical Behavior of... Feb 2024Aseptic loosening due to mechanical failure of bone cement is considered to be a leading cause of revision of joint replacement systems. Detailed quantified information...
Aseptic loosening due to mechanical failure of bone cement is considered to be a leading cause of revision of joint replacement systems. Detailed quantified information on the number, size and distribution pattern of pores can help to obtain a deeper understanding of the bone cement's fatigue behavior. The objective of this study was to provide statistical descriptions for the pore distribution characteristics of laboratory bone cement specimens with different amounts of antibiotic contents. For four groups of bone cement (Palacos) specimens, containing 0.3, 0.6, 1.2 and 2.4 wt/wt% of telavancin antibiotic, seven samples per group were micro computed tomography scanned (38.97 μm voxel size). The images were first preprocessed in Mimics and then analyzed in Dragonfly, with the level of threshold being set such that single-pixel pores become visible. The normalized pore volume data of the specimens were then used to extract the logarithmic histograms of the pore densities for antibiotic groups, as well as their three-parameter Weibull probability density functions. Statistical comparison of the pore distribution data of the antibiotic groups using the Mann-Whitney non-parametric test revealed a significantly larger porosity (p < 0.05) in groups with larger added antibiotic contents (2.4 and 0.6 wt/wt% vs 0.3 wt/wt%). Further analysis revealed that this effect was associated with the significantly larger frequency of micropores of 0.1-0.5 mm diameter (p < 0.05) in groups with larger antibiotic content (2.4 wt/wt% vs and 0.6 and 0.3 wt/wt%), implying that the elution of the added antibiotic produces micropores in this diameter range mainly. Based on this observation and the fatigue test results in the literature, it was suggested that micropore clusters have a detrimental effect on the mechanical properties of bone cement and play a major role in initiating fatigue cracks in highly antibiotic added specimens.
Topics: Animals; Polymethyl Methacrylate; Anti-Bacterial Agents; Bone Cements; Odonata; X-Ray Microtomography; Statistical Distributions
PubMed: 38100980
DOI: 10.1016/j.jmbbm.2023.106297 -
Parasitology Sep 2015Visual displays of data in the parasitology literature are often presented in a way which is not very informative regarding the distribution of the data. An example... (Review)
Review
Visual displays of data in the parasitology literature are often presented in a way which is not very informative regarding the distribution of the data. An example being simple barcharts with half an error bar on top to display the distribution of parasitaemia and biomarkers of host immunity. Such displays obfuscate the shape of the data distribution through displaying too few statistical measures to explain the spread of all the data and selecting statistical measures which are influenced by skewness and outliers. We describe more informative, yet simple, visual representations of the data distribution commonly used in statistics and provide guidance with regards to the display of estimates of population parameters (e.g. population mean) and measures of precision (e.g. 95% confidence interval) for statistical inference. In this article we focus on visual displays for numerical data and demonstrate such displays using an example dataset consisting of total IgG titres in response to three Plasmodium blood antigens measured in pregnant women and parasitaemia measurements from the same study. This tutorial aims to highlight the importance of displaying the data distribution appropriately and the role such displays have in selecting statistics to summarize its distribution and perform statistical inference.
Topics: Computer Graphics; Data Interpretation, Statistical; Female; Humans; Parasitology; Pregnancy; Publications; Statistical Distributions
PubMed: 26118403
DOI: 10.1017/S0031182015000748 -
Anaesthesia Feb 2017
Topics: Computer Simulation; Models, Statistical; Normal Distribution
PubMed: 28093738
DOI: 10.1111/anae.13795 -
Journal of Theoretical Biology Oct 2016Sequence comparison has become an essential tool in bioinformatics, because highly homologous sequences usually imply significant functional or structural similarity.... (Comparative Study)
Comparative Study
Sequence comparison has become an essential tool in bioinformatics, because highly homologous sequences usually imply significant functional or structural similarity. Traditional sequence analysis techniques are based on preprocessing and alignment, which facilitate measuring and quantitative characterization of genetic differences, variability and complexity. However, recent developments of next generation and whole genome sequencing technologies give rise to new challenges that are related to measuring similarity and capturing rearrangements of large segments contained in the genome. This work is devoted to illustrating different methods recently introduced for quantifying sequence distances and variability. Most of the alignment-free methods rely on counting words, which are small contiguous fragments of the genome. Our approach considers the locations of nucleotides in the sequences and relies more on appropriate statistical distributions. The results of this technique for comparing sequences, by extracting information and comparing matching fidelity and location regularization information, are very encouraging, specifically to classify mutation sequences.
Topics: Algorithms; Base Sequence; Cluster Analysis; Genome; Genome, Bacterial; Genome, Mitochondrial; Genomics; Herpesviridae; Mutation; Phylogeny; Statistical Distributions
PubMed: 27460589
DOI: 10.1016/j.jtbi.2016.07.032 -
ENeuro Jan 2023A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior... (Review)
Review
A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.
Topics: Bayes Theorem; Probability; Sensation; Normal Distribution
PubMed: 36316119
DOI: 10.1523/ENEURO.0144-22.2022 -
Analysis of occupational radiation dose data and determination of suitable probability distribution.Radiation Protection Dosimetry Jul 2023The first study on fitting dose data for workers was performed by Gale( 1) in 1965 where log-normal and normal distributions were used. Since then, various models of...
The first study on fitting dose data for workers was performed by Gale( 1) in 1965 where log-normal and normal distributions were used. Since then, various models of dose distribution have been proposed. The log-normal distribution and its different forms have been widely used for fitting the dose data. Most of the studies included one or two distributions under consideration. In this study five distributions are considered for fitting and four distributions are selected based on observation of Cullen-Frey graph. The Akaike's Information criteria (AIC) and Bayesian Information criteria (BIC) are applied to find the suitable distribution to fit the occupational dose data. The maximum likelihood method was used for calculation of AIC, BIC values and parameter estimation. A computer code is written in R-language and environment for statistical computing and graphics for analysis of occupational dose data of three institutions.
Topics: Humans; Bayes Theorem; Probability; Normal Distribution; Statistical Distributions; Radiation Dosage
PubMed: 37259618
DOI: 10.1093/rpd/ncad160 -
Biometrics Dec 2018The box-and-whiskers plot is an extraordinary graphical tool that provides a quick visual summary of an observed distribution. In spite of its many extensions, a really...
The box-and-whiskers plot is an extraordinary graphical tool that provides a quick visual summary of an observed distribution. In spite of its many extensions, a really suitable boxplot to display circular data is not yet available. Thanks to its simplicity and strong visual impact, such a tool would be especially useful in all fields where circular measures arise: biometrics, astronomy, environmetrics, Earth sciences, to cite just a few. For this reason, in line with Tukey's original idea, a Tukey-like circular boxplot is introduced. Several simulated and real datasets arising in biology are used to illustrate the proposed graphical tool.
Topics: Computational Biology; Computer Graphics; Computer Simulation; Data Display; Data Interpretation, Statistical; Datasets as Topic; Humans; Statistical Distributions
PubMed: 29782636
DOI: 10.1111/biom.12889 -
Journal of Healthcare Engineering 2022At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations....
At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations. Therefore, it can provide great help in clinical medicine to give early warning and plan coping plans according to different situations. This paper studied an obstetrics central surveillance system based on a medical image segmentation algorithm. Images obtained by central obstetrics monitoring are segmented, magnified in detail, and image features are extracted, collated, and trained. The normal distribution rule is used to classify the features, which are included in the feature library of the obstetric central monitoring system. In the gray space of the medical image, the statistical distribution of gray features of the medical image is described by the mixture model of Rayleigh distribution and Gaussian distribution. In the gray space of the medical image, Taylor series expansion is used to describe the linear geometric structure of medicine. The eigenvalues of Hessian matrix are introduced to obtain high-order multiscale features of medicine. The multiscale feature energy function is introduced into Markov random energy objective function to realize medical image segmentation. Compared with other segmentation algorithms, the accuracy and sensitivity of the proposed algorithm are 87.98% and 86.58%, respectively, which can clearly segment small medical features.
Topics: Algorithms; Humans; Image Processing, Computer-Assisted; Normal Distribution
PubMed: 35529540
DOI: 10.1155/2022/3545831 -
Clinica Chimica Acta; International... Dec 2021The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one...
The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.
Topics: Humans; Normal Distribution; Reference Values
PubMed: 34653386
DOI: 10.1016/j.cca.2021.10.006