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作者： Alghamdi, Amani Bowling Green State University

学位级别：Ph.D.

Generalizations of univariate distributions are often of interest to serve for real life phenomena. These generalized distributions are very useful in many fields such as medicine, physics, engineering and biology. Lomax distribution (Pareto-II) is one of the well known univariate distributions that is considered as an alternative to the exponential, gamma, and Weibull distributions for heavy tailed data. However, this distribution does not grant great flexibility in modeling data. In this dissertation, we introduce a generalization of the Lomax distribution called Rayleigh Lomax (RL) distribution using the form obtained by El-Bassiouny et al. (2015). This distribution provides great fit in modeling wide range of real data sets. It is a very flexible distribution that is related to some of the useful univariate distributions such as exponential, Weibull and Rayleigh distributions. Moreover, this new distribution can also be transformed to a lifetime distribution which is applicable in many situations. For example, we obtain the inverse estimation and confidence intervals in the case of progressively Type-II right censored situation. We also apply Schwartz information approach (SIC) and modified information approach (MIC) to detect the changes in parameters of the RL distribution. The performance of these approaches is studied through simulations and applications to real data sets. According to Aryal and Tsokos (2009), most of the real world phenomenon that we need to study are asymmetrical, and the normal model is not a good model for studying this type of dataset. Thus, skewed models are necessary for modeling and fitting asymmetrical datasets. Azzalini (1985) introduced the univariate skew normal distribution and his approach can be applied in any symmetrical model. However, if the underlying (base) probability is not symmetric, we can not apply the Azzalini’s approach. This motivated the study for more flexible alternative. Shaw and Buckley (2007) introduced a qu

关键词： Confidence Intervals Inverse estimator Order statistics Proportional hazard family Change point Rayleigh Lomax distribution Information criterion binary segmentation

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Některé postupy pro detekce změn ve statistických modelech

Některé postupy pro detekce změn ve statistických modele...

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作者： Marešová, Linda Charles University of Prague

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Rôzne metódy odhadu bodu zmeny

Rôzne metódy odhadu bodu zmeny

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作者： Šimonová, Soňa Charles University of Prague

This thesis aims to give a comprehensive account of some of the most recent methods of a change point estimation. The literature on the change point estimation shows a variety of approaches to deal with this subject. Among them, tests based on the popular CUSUM process, likelihood ratio tests, wild binary segmentation and some of the most recent techniques on the change point estimation in panel data are all covered by this paper. The case of dependent panels is discussed as well. The practical part of the study is focused on application of the wild binary segmentation method on weekly log-returns of the Dow Jones stock index. Firstly, we fit a GARCH model to the analysed time series. We next use the wild binary segmenatation method to detect structural changes in the mean of the original time series. Next, we apply the same method to the residuals from the GARCH fit. We analyse several penalization criteria proposed by previous studies and evaluate their effects on the estimated number and locations of the change points in the given data set. 1

关键词： change point CUSUM statistic likelihood ratio binary segmentation panel data boundary issue bootstrap dependent data

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Automatic Identification of Strong Energy Noise in Seismic Data Based on U-Net

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Sun, Luping Zheng, Xiaodong Ding, Ling Shou, Hao Li, Hao China Univ Geosci Key Lab Intraplate Volcanoes & Earthquakes Minist Educ Beijing 100083 Peoples R China China Univ Geosci Sch Geophys & Informat Technol Beijing 100083 Peoples R China Res Inst Petr Explorat & Dev RIPED Beijing 100083 Peoples R China

Strong energy noise is one of the typical interferences in seismic exploration, which is usually attenuated with custom threshold methods. The key to ideal denoising is to accurately identify the zones of strong energy noise in seismic records and then determine the reasonable denoising thresholds. However, due to the influence of surface and acquisition conditions, the distribution and characteristics of strong energy noise in seismic records vary greatly and constantly change over time and space. In practice, repeated experiments are required to determine the noise zones and reasonable thresholds. In this study, we propose a new method that utilizes U-Net deep learning to automatically identify strong energy noise in seismic data. Our trained network could accurately identify the intrinsic characteristics of strong energy interference in seismic data and adapt to the spatiotemporal variability of noise, thus facilitating more efficient and accurate determination of denoising thresholds. The method transformed the identification of strong energy noise into a binary segmentation task of the image and solved it by U-Net. The practical applications validated that this approach significantly improved the accuracy of strong energy denoising under complex near-surface conditions while reducing the time and labor cost of analyzing and testing denoising parameters, and markedly improving efficiency.

关键词： Artificial seismic data binary segmentation denoising strong energy noise threshold U-Net

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A comparison of single and multiple changepoint techniques for time series data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2022年 170卷 107433-107433页

作者： Shi, Xuesheng Gallagher, Colin Lund, Robert Killick, Rebecca Clemson Univ Sch Math & Stat Clemson SC 29634 USA Univ Calif Santa Cruz Dept Stat Santa Cruz CA 95064 USA Univ Lancaster Dept Math & Stat Lancs LA1 4YF England

Correlated time series data arise in many applications. This paper describes and compares several prominent single and multiple changepoint techniques for correlated time series. In the single changepoint problem, various cumulative sum (CUSUM) and likelihood ratio statistics, along with boundary cropping scenarios and scaling methods (e.g., scaling to an extreme value or Brownian Bridge limit) are compared. A recently developed test based on summing squared CUSUM statistics over all time indices is shown to have controlled Type I error and superior detection power. In the multiple changepoint setting, penalized likelihoods drive the discourse, with AIC, BIC, mBIC, and MDL penalties being considered. binary and wild binary segmentation techniques are also compared. A new distance metric is introduced that measures differences between two multiple changepoint segmentations. Algorithmic and computational concerns are discussed and simulations are given to support all conclusions. In the end, the multiple changepoint setting admits no clear methodological winner, performance depending on the particular scenario. Nonetheless, some practical guidance emerges.(c) 2022 Elsevier B.V. All rights reserved.

关键词： AMOC techniques ARMA models binary segmentation Brownian bridge CUSUM tests Likelihood ratio test Minimum description length One-step-ahead prediction residuals Penalized likelihoods Wild binary segmentation

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Rank-statistics based enrichment-site prediction algorithm developed for chromatin immunoprecipitation on chip experiments

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BMC BIOINFORMATICS 2006年第1期7卷 434-434页

作者： Ghosh, Srinka Hirsch, Heather A. Sekinger, Edward Struhl, Kevin Gingeras, Thomas R. Affymetrix Inc Santa Clara CA 95051 USA Harvard Univ Sch Med Dept Biol Chem & Mol Pharmacol Boston MA 02115 USA Amb Inc Austin TX 78744 USA

Background: High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. Tiling arrays are increasingly used in chromatin immunoprecipitation (IP) experiments (ChIP on chip). ChIP on chip facilitates the generation of genome-wide maps of in-vivo interactions between DNA-associated proteins including transcription factors and DNA. Analysis of the hybridization of an immunoprecipitated sample to a tiling array facilitates the identification of ChIP-enriched segments of the genome. These enriched segments are putative targets of antibody assayable regulatory elements. The enrichment response is not ubiquitous across the genome. Typically 5 to 10% of tiled probes manifest some significant enrichment. Depending upon the factor being studied, this response can drop to less than 1%. The detection and assessment of significance for interactions that emanate from non-canonical and/or un-annotated regions of the genome is especially challenging. This is the motivation behind the proposed algorithm. Results: We have proposed a novel rank and replicate statistics-based methodology for identifying and ascribing statistical confidence to regions of ChIP-enrichment. The algorithm is optimized for identification of sites that manifest low levels of enrichment but are true positives, as validated by alternative biochemical experiments. Although the method is described here in the context of ChIP on chip experiments, it can be generalized to any treatment-control experimental design. The results of the algorithm show a high degree of concordance with independent biochemical validation methods. The sensitivity and specificity of the algorithm have been characterized via quantitative PCR and independent computational approaches. Conclusion: The algorithm rank

关键词： Perfect Match Tiling Array binary segmentation Putative Site Probe Affinity

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Interactive image segmentation based on the appearance model and orientation energy

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COMPUTER VISION AND IMAGE UNDERSTANDING 2022年第0期217卷 103371-103371页

作者： Qu, Shaojun Tan, Huang Li, Qiaoliang Peng, Zili Hunan Normal Univ 36 Lushan Rd Changsha 410081 Peoples R China Hunan Youmei Technol Dev Co Ltd Chuanggu Ind Pk568 Queyuan Rd Changsha Peoples R China

Tang et al. (2013) proposed a graph-based image segmentation model by minimizing the distance between the object and background appearance overlap models. This model is very effective for interactive image segmentation. However, it is prone to isolated nodes when the colors or other appearances characteristic of the object and background are very similar. To improve the performance of this algorithm and related algorithms, we add new spatial distance and contour orientation energy terms to the energy function. Accordingly, we modify the construction of the energy graph. We add terminal nodes S and T and add prior constraints. Finally, we use the pseudoflow algorithm proposed by Hochbaum to calculate the maximum flow of the new energy graph. A large number of experiments on the MSRA dataset, BSD dataset and GrabCut dataset show that the results of the proposed method are better than those of many recently proposed image segmentation methods. The code is available at https://***/powerhope/AMOE.

关键词： Graph cut Appearance model Orientation energy Pseudoflow Interactive image segmentation binary segmentation

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Intelligent tuberculosis activity assessment system based on an ensemble of neural networks

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105800-105800页

作者： Sineglazov, Victor Riazanovskiy, Kirill Klanovets, Alexander Chumachenko, Elena Linnik, Nikolai Natl Aviat Univ 1 Liubomyra Huzara Ave Kiev UA-03058 Ukraine Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst 37 Peremohy Ave Kiev UA-03056 Ukraine FG Yanovsky Natl Acad Med Sci Ukraine Natl Inst Phthisiol & Pulmonol 10 N Amosova Str Kiev UA-03038 Ukraine

This article proposes a novel approach to assess the degree of activity of pulmonary tuberculosis by active tuberculoma foci. It includes the development of a new method for processing lung CT images using an ensemble of deep convolutional neural networks using such special algorithms: an optimized algorithm for preliminary segmentation and selection of informative scans, a new algorithm for refining segmented masks to improve the final accuracy, an efficient fuzzy inference system for more weighted activity assessment. The approach also includes the use of medical classification of disease activity based on densitometric measures of tuberculomas. The selection and markup of the training sample images were performed manually by qualified pulmonologists from a base of approximately 9,000 CT lung scans of patients who had been enrolled in the dispensary for 15 *** first basic step of the proposed approach is the developed algorithm for preprocessing CT lung scans. It consists in segmentation of intrapulmonary regions, which contain vessels, bronchi, lung walls to detect complex cases of ingrown *** minimize computational cost, the proposed approach includes a new method for selecting informative lung scans, i.e., those that potentially contain *** main processing step is binary segmentation of tuberculomas, which is proposed to be performed optimally by a certain ensemble of neural networks. Optimization of the ensemble size and its composition is achieved by using an algorithm for calculating individual contributions. A modification of this algorithm using new effective heuristic metrics has been proposed which improves the performance of the algorithm for this problem.A special algorithm was developed for post-processing of tuberculoma masks obtained during the segmen-tation step. The goal of this step is to refine the calculated mask for the physical placement of the tuberculoma. The algorithm consists in cleaning the mask from noisy

关键词： Convolutional neural networks binary segmentation Computed tomography Pulmonary tuberculosis Inflammatory process activity Fuzzy inference

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Deep-learning framework for optimal selection of soil sampling sites

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 217卷

作者： Pham, Tan-Hanh Acharya, Praneel Bachina, Sravanthi Osterloh, Kristopher Nguyen, Kim-Doang Florida Inst Technol Dept Mech & Civil Engn Melbourne FL 32091 USA Minnesota State Univ Dept Mech Engn Mankato MN USA South Dakota State Univ Dept Agron Hort & Plant Sci Brookings SD USA

Soil sampling is one of the most fundamental agricultural processes. The selection of soil sampling locations within a field plays a crucial role in soil -health analysis. The current soil -sampling techniques produce random samples across a field, which are not representative of the field conditions. This work leverages the recent advancements of deep learning in image processing for finding optimal locations that present the important characteristics of a field. The data for training are collected at different fields in local farms with five features: aspect, flow accumulation, slope, NDVI (normalized difference vegetation index), and yield. The soil sampling dataset is challenging because of the unbalance between the number of pixels that represent soilsampling spots and the number of pixels that represent the background area. In this work, we approach the problem with two methods, the first approach involves utilizing a state-of-the-art model with the convolutional neural network (CNN) backbone, while the second is to innovate a deep -learning design grounded in the concepts of transformer and self -attention. Our framework is constructed with an encoder-decoder architecture with the self -attention mechanism as the backbone. In the encoder, the self -attention mechanism is the key feature extractor, which produces feature maps. In the decoder, we introduce atrous convolution networks to concatenate, fuse the extracted features, and then export the optimal locations for soil sampling. Currently, the model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%, while the performance metrics of the state-of-the-art CNN -based model are 66.08%, 3.85%, and 1.98%, respectively. This indicates that our proposed model outperforms the CNN -based method on the soil -sampling dataset. To the best of our knowledge, our work is the first to provide a soil -

关键词： Deep learning binary segmentation Self-attention Class imbalanced segmentation Soil sampling Precision agriculture

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A Likelihood-Based Multiple Change Point Algorithm for Count Data with Allowance for Over-Dispersion

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Open Journal of Statistics 2024年第5期14卷 518-545页

作者： Shalyne Nyambura Anthony Waititu Antony Wanjoya Herbert Imboga School of Mathematics & Physical Sciences Jomo Kenyatta University of Agriculture & Technology Juja Kenya

Count data is almost always over-dispersed where the variance exceeds the mean. Several count data models have been proposed by researchers but the problem of over-dispersion still remains unresolved, more so in the context of change point analysis. This study develops a likelihood-based algorithm that detects and estimates multiple change points in a set of count data assumed to follow the Negative Binomial distribution. Discrete change point procedures discussed in literature work well for equi-dispersed data. The new algorithm produces reliable estimates of change points in cases of both equi-dispersed and over-dispersed count data;hence its advantage over other count data change point techniques. The Negative Binomial Multiple Change Point Algorithm was tested using simulated data for different sample sizes and varying positions of change. Changes in the distribution parameters were detected and estimated by conducting a likelihood ratio test on several partitions of data obtained through step-wise recursive binary segmentation. Critical values for the likelihood ratio test were developed and used to check for significance of the maximum likelihood estimates of the change points. The change point algorithm was found to work best for large datasets, though it also works well for small and medium-sized datasets with little to no error in the location of change points. The algorithm correctly detects changes when present and fails to detect changes when change is absent in actual sense. Power analysis of the likelihood ratio test for change was performed through Monte-Carlo simulation in the single change point setting. Sensitivity analysis of the test power showed that likelihood ratio test is the most powerful when the simulated change points are located mid-way through the sample data as opposed to when changes were located in the periphery. Further, the test is more powerful when the change was located three-quarter-way through the sample data compared to when

关键词： Over-Dispersion Multiple Changepoint binary segmentation Likelihood Ratio Test

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