Statistical Methods In Terms Research Articles

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set.

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Comparison of Oncologic Outcomes Between Radiation Therapy and Transoral Laser Microsurgery for Early Glottic and Supraglottic Squamous Cell Carcinoma; a Retrospective Cohort Study with Literature Review

Background: Laryngeal cancer is known as the second most common airway cancer. These tumors are usually diagnosed early; thus, it is important to choose the optimal treatment modality. Several studies are comparing transoral laser microsurgery (TLM) and radiotherapy in early-stage laryngeal cancer. Due to a paucity of high-quality research and the lack of conclusive randomized prospective studies, standard care for early glottic cancer is still a matter of controversy. Methods: In this retrospective cohort study, T1-T2N0M0 laryngeal cancer patients who underwent TLM or radiotherapy were compared with statistical methods in terms of Recurrence Rate, Organ (Larynx) Preservation Rate, Mortality Rate, and Overall Survival. Results: A total of 123 patients were evaluated in this study, 65 of whom underwent TLM and 58 underwent radiotherapy. There was no significant difference in demographic and medical characteristics of patients. The rate of recurrence was 27.3% in the TLM group and 43.6% in the radiotherapy group. There was no significant difference between the two groups (P = 0.114). Overall mortality rate (5.4% vs 39.6%, P = 0.001), Disease-specific mortality rate (5.5% vs 39.9%, P = 0.001), and aorgan preservation rate (98.5%vs 89.7%, P = 0.035) were all in favor of TLM. Conclusions: TLM appears to be the treatment of choice in patients with early laryngeal cancer due to a better overall survival rate, lower disease-specific mortality rate, and better organ preservation rate than definitive radiotherapy.

Concurrent Multilayer Fault Monitoring with Nonlinear Latent Variable Regression

Latent variable regression (LVR) constructs the latent structure by maximizing the projection of quality variables on the latent spaces of the process variables, which has demonstrated its effectiveness over other multivariate statistical methods in terms of quality-relevant monitoring. However, it cannot be applied to model most real-world industrial processes due to its implicit linear assumption on the data. To overcome this issue, the kernel trick is designed to develop its nonlinear counterpart. Further, a novel concurrent kernel LVR (CKLVR) method is proposed for multilayer modeling with subsequent decomposition, which separates the process and data spaces comprehensively into five subspaces. The corresponding monitoring statistics are also developed for each subspace extracted by CKLVR, with each serving a particular monitoring purpose. Case studies including a numerical simulation, Dow’s Refining Process, and the Tennessee Eastman Process are employed to illustrate the performance of the proposed algorithm.

Synthesis of Planar Circular Arrays with Quantized Amplitude Weights.

A new stepwise and radially processed method for synthesizing uniformly distributed circular planar arrays with quantized weights is proposed in this paper. This method is based on a generalized analytical equation describing that for high directivity focusing arrays, minimizing the weighted mean square error between the reference pattern and the synthesized pattern is equivalent to minimizing the mean square error between the radial cumulative distributions of the reference distribution and the synthesized distribution. This principle has been successfully performed for designing large concentric ring arrays, and in this paper, we extend its use for synthesizing uniformly distributed planar circular arrays with quantized weights. Various numerical examples and comparisons with several reported statistical methods in terms of the lowest Maximum SideLobe Level (MSLL) demonstrate the effectiveness of the proposed method.

A Study on Sensor System Latency in VR Motion Sickness

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error.

Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data.

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

Evaluation of Acromegaly patients with sleep disturbance related symptoms.

Background and Objectives:It is known that the prevalence of obstructive sleep apnea (OSA) is increased in acromegaly. Craniofacial anomalies, macroglossia, and thickening of the laryngeal wall caused by the increase in soft tissue in these patients lead to OSA. Also, the increase in growth hormone can trigger central apnea by causing a decrease in respiratory drive. Determining the polysomnographic characteristics of acromegaly patients is important to reveal the effect of these mechanisms.Methods:The demographic and polysomnographic characteristics of 33 acromegaly patients who underwent polysomnography (PSG) with suspicion of sleep disorders between 2011 and 2018 in the sleep laboratory of our hospital were retrospectively analyzed. One of the patients was excluded from the analysis because PSG was performed in the postoperative period. The remaining 32 patients with active acromegaly were grouped according to their gender and the presence of OSA and compared with statistical methods in terms of polysomnographic and clinical features.Results:OSA (AHI>5) was detected in 78.1% of 32 active acromegaly patients (18 females, 14 males) who underwent PSG with suspicion of sleep-disordered breathing. Moderate-severe OSA (62.5%) was found in most patients, and there was no difference between the sexes in terms of OSA detection rate and OSA severity. Respiratory events appear to be predominantly obstructive hypopneas. Also, the polysomnographic features of female and male acromegaly patients with OSA were found to be similar. It is seen that the OSA group is similar to the group with simple snoring in terms of body mass index (BMI), but is statistically significantly older (p=0,007). A positive correlation was found between age and AHI in pairwise correlation analysis (r:0,426 p:0,015, respectively).Conclusion:Considering that the prevalence of OSA in the population is approximately 5%, our results show that the risk of OSA in acromegaly increases, and obstructive pathways are effective in this increase. The probability of OSA occurrence and polysomnographic features between the genders are similar. Although the median BMI of the patients with and without OSA was similar, the median age was higher in the group with OSA, middle-aged acromegaly patients should be evaluated in terms of OSA even if there is no obvious obesity.

Determination of Turkey Province with Multivariate Statistical Methods in terms of Banking Activities

Bu çalışmanın amacı, bankacılık verilerinden oluşan değişkenleri kullanarak çok değişkenli istatistiksel analiz yöntemleri ile Türkiye’deki iller arasındaki benzerlik ve farklılıkları ortaya koymaktır. Bu bağlamda 2018 yılına ilişkin 10 farklı bankacılık göstergesi ile 81 il için analizler gerçekleştirilmiştir. Çalışmada ilk aşama olarak, çok değişkenli istatistiksel analiz yöntemlerinden temel bileşenler analizi (TBA) uygulanmış, sonrasında hem ham veriler hem de TBA ile elde edilen bileşenler kullanılarak kümeleme analizi yapılmıştır. Her iki durumda elde edilen sonuçlar kıyaslanmıştır.

Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees

AbstractLittle Cottonwood Canyon Highway is a dead‐end, two‐lane road leading to Utah's Alta and Snowbird ski resorts. It is the only road access to these resorts and is heavily traveled during the ski season. Professional avalanche forecasters monitor this road throughout the ski season in order to make road closure decisions in the face of avalanche danger. Forecasters at the Utah Department of Transportation (UDOT) avalanche guard station at Alta have maintained an extensive daily winter database on explanatory variables relating to avalanche prediction. Whether or not an avalanche crosses the road is modeled in this paper via Bayesian additive tree methods. Utilizing daily winter data from 1995 to 2011, results show that using Bayesian tree analysis outperforms traditional statistical methods in terms of realized misclassification costs that take into consideration asymmetric losses arising from two types of error. Closing the road when an avalanche does not occur is an error harmful to resort owners, and not closing the road when one does may result in injury or death. Copyright © 2016 John Wiley & Sons, Ltd.

Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns

Abstract-Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the-art statistical methods in terms of both accuracy and training time.

A spiking neural network (SNN) forecast engine for short-term electrical load forecasting

Short-term load forecasting (STLF) is one of the planning strategies adopted in the daily power system operation and control. All though many forecasting models have been developed through the years, the uncertainties present in the load profile significantly degrade the performance of these models. The uncertainties are mainly due to the sensitivity of the load demand with varying weather conditions, consumption pattern during month and day of the year. Therefore, the effect of these weather variables on the load consumption pattern is discussed. Based on the literature survey, artificial neural networks (ANN) models are found to be an alternative to classical statistical methods in terms of accuracy of the forecasted results. However, handling of bulk volumes of historical data and forecasting accuracy is still a major challenge. The development of third generation neural networks such as spike train models which are closer to their biological counterparts is recently emerging as a robust model. So, this paper presents a load forecasting system known as the SNNSTLF (spiking neural network short-term load forecaster). The proposed model has been tested on the database obtained from the Australian Energy Market Operator (AEMO) website for Victoria State.

TEMPORAL AND SPATIAL EVOLUTION OF EXTREME EVENTS

In this study two statistical methods for computing time- and space-evolving extreme events are implemented and assessed. The implementation and application of the methods are carried out with a view towards the safety assessment of water defences. With this type of application in mind, the methods are used to produce time- and space-varying extreme wind fields, which are needed to force wave and hydrodynamic models. Furthermore, the results of the two methods are compared and effects of the choice of distribution, threshold and reference location on
 the resulting wind fields are assessed. Although the wind fields produced by the methods show more erratic spatial variations, due to the associated uncertainties, than those of the observed wind fields, they seem realistic and can in principle be used to drive wave and hydrodynamic models. However, an assessment of the results of these statistical methods in terms of the underlying physics still needs to be carried out and is part of future work.

Plant–arthropod associations from the Early Miocene of the Most Basin in North Bohemia—Palaeoecological and palaeoclimatological implications

Terrestrial plants and insects account for the majority of the Earth's biodiversity today, and herbivorous interactions are dated back more than 400 million yr. However, investigation of their associations remains in its infancy in Europe. The Miocene is characterized by palaeogeographic re-organization due to the collision of the African with the Eurasian plates. Antarctica's enormous impact on global climatic conditions, and thus on European palaeoenvironment, resulted from a series of episodes of minor glaciations in the Early Miocene after the initial cooling and ice sheet formed during the Oligocene.More than 3500 plant remains showing various kinds of feeding damage were available for the present study. These trace fossils are classified according to their external morphology into damage types (DT) and grouped to functional feeding-groups. The Neogene plant record in Europe is rich and diverse, offering a profound large-scale understanding of the floristic and vegetational development. A database of fossil traces from the Most Basin was compiled and analyzed by various statistical methods in terms of the diversity and intensity of palaeoherbivory. The primary objective is to present results on the development of insect herbivory through the section of the Bílina Mine in North Bohemia, with the aim of understanding the principal factors that caused the observed phenomena. The research was focused on two horizons—Delta Sandy Horizon (DSH) and Lake Clayey Horizon (LCH)—both sufficiently represented to compare their palaeoecological and palaeoclimatological signals on the basis of the presence of damages caused by insects and other herbivorous arthropods. A total sample of 60 different damage types, attributed to eight main functional feeding groups, was examined. Results from analyses of the frequency and diversity of the selected categories of plant arthropod associations within both examined horizons significantly support different environmental conditions. The LCH seems to be affected by the relatively colder and drier climatic conditions as indicated by a four times greater frequency of leaves with galls and lower taxonomic diversity and species equability, whereas DSH indicates warmer and more humid conditions reflecting the higher diversity of the plant species and damage types.

Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in terms of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in terms of strategies used. Furthermore, we discussed them in terms of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.

Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in terms of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in terms of strategies used. Furthermore, we discussed them in terms of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.

Dynamical versus statistical downscaling methods for ocean wave heights

AbstractIn this study, dynamical and statistical downscaling methods for estimating seasonal statistics of significant wave heights (SWH) were intercompared, with the downscaling results being evaluated against the ERA40 wave data in terms of climatological characteristics and interannual variability. It was also shown that biases in climate‐model‐simulated climate and variability of the atmospheric circulation (or predictors in general) can result in large biases in the estimated climate and variability of SWH (or the predictand in general), and that such biases can be effectively diminished by using standardized predictor quantities in statistical downscaling models. In dynamical downscaling, however, model variability biases remain to be dealt with, whereas the effects of model climate biases can be reduced to some extent by replacing the climate‐model‐simulated wind climate with the observed one. Therefore, the dynamical approach was found to be not as good as the statistical methods in terms of reproducing the observed climate and interannual variability of the predictand, although it bears substantial similarity to the statistical methods in terms of projected possible future changes. Also, it was shown that the observed interannual variability of seasonal statistics (including extremes) can be better reproduced by using 12‐hourly, rather than seasonal, data in statistical downscaling. This stresses the importance of availability of higher‐resolution data from climate model outputs. Nevertheless, a non‐stationary extreme value model with covariates was found to be the best in reproducing the observed climate of extremes. All the statistical downscaling methods and the intercomparison results are applicable to other climate variables (not limited to ocean wave heights). Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

Reliability Analysis of Non‐Parametric Statistical Tests for the Evaluation of Linear Drift in Experimental Data

Abstract: During usual data gathering, the statistical analysis efficiency strongly depends on the noise level superimposed on the signal. It has been found that some well known statistical tests, commonly utilised in data acquisition in order to detect the presence of drift, can fail under some conditions. Thus, a statistical procedure for the predictive reliability estimation of the utilised statistical method could be useful in the design of experimental analysis. This paper reports the results of a simulation study carried out to evaluate the performance in drift detection of non‐parametric tests such as the Wald‐Wolfowitz run test, in comparison with the Mann‐Whitney, reverse arrangement test. In order to detect the sensitivity of the tests to evaluate a monotonous drift, a simulation program was developed. In the program a Gaussian raw data sequence with a linear pattern of variable slope and with variable variance was simulated and given as the input to the tests. The capability to detect the presence of drift as a function of angular coefficient and variance of the noise superimposed on the signal was verified. The obtained data were synthesised in graphs so that the experimentalist could determine preliminarily the effectiveness of each of the considered statistical methods in terms of percentage of success in detecting the presence of drift phenomena as a function of drift relevance and the noise amplitude. Finally, the graphs permitted the elucidation of the causes of contradictory failing results observed in long term experimental analysis.

Parallel consensual neural networks

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.