Fisher Score Method Research Articles

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features

BackgroundThis study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). MethodsNineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel–Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. ResultsThe prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. ConclusionsThe model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network

This paper investigates how the Fisher score feature selection approach can be used with capsule networks for diabetes detection. It also evaluates how well this algorithm works based on a number of evaluation parameters. The selected features using Fisher score method was then employed to train a capsule network model. Accuracy (94%), precision (94%0, recall (94%), F1 score (94%), and other performance evaluation metrics were thoroughly analyzed to determine the algorithm's efficacy. The results demonstrated that the combination of Fisher score feature selection and capsule networks yielded promising performance in diabetes detection. The selected features effectively captured the relevant information necessary for accurate classification The capsule network model was very accurate, which shows that it could be a good tool for diagnosing diabetes. Also, the accuracy and recall values showed that the algorithm could correctly place both positive and negative cases of diabetes, minimizing the risk of misdiagnosis. By merging the Fisher score feature selection approach with capsule networks, this research study contributes to advancing diabetes detection.

Attack detection analysis in software-defined networks using various machine learning method

The Software-Defined Network (SDN) provides a more flexible and effectively managed network design for next-generation networking. Network managers can easily manage and regulate the entire network using its programmable central controller architecture. This central controller serves as the focal point for numerous attack vectors due to its centralized structure. However, Distributed Denial of Service (DDoS) attacks against the SDN is the most prominent. The goal of this project is to use a machine learning method to categorize SDN traffic as either attack or normal traffic. Next, the Feature Selection method, such as the Filter-based Fisher score method, Wrapper-based method, and analysis of variables (ANOVA) f-test, is used for finely-granulated detection. Then, a rule-based detection method using the Renyi joint entropy algorithm is employed to detect DDoS attacks on SDN controllers. We manage a public "DDoS attack SDN Dataset" with 23 attributes overall. The dataset includes normal and attack traffic for the Internet Control Message Protocol (ICMP), User Datagram Protocol (UDP), and Transmission Control Protocol (TCP). Except for attributes that specify the target and source machines, the dataset, which contains more than 100,000 recordings, has statistical features such as byte count, duration sec, packet rate, and packet per flow. In the classification process, many classifiers such as Artificial Neural Network (ANN), XGBoost (XGB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) were used. The test results demonstrated the efficacy and efficiency of the suggested strategy using the analysis of variables (ANOVA), which performed better than competing methods across a range of evaluation parameters.

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods.

Non-Bayesian Parametric Missing-Mass Estimation

We consider the classical problem of missing-mass estimation, which deals with estimating the total probability of unseen elements in a sample. The missing-mass estimation problem has various applications in machine learning, statistics, language processing, ecology, sensor networks, and others. The naive, constrained maximum likelihood (CML) estimator is inappropriate for this problem since it tends to overestimate the probability of the observed elements. Similarly, the conventional constrained Cramer-Rao bound (CCRB), which is a lower bound on the mean-squared-error (MSE) of unbiased estimators, does not provide a relevant bound on the performance for this problem. In this paper, we introduce a frequentist, non-Bayesian parametric model of the problem of missing-mass estimation. We introduce the concept of missing-mass unbiasedness by using the Lehmann unbiasedness definition. We derive a non-Bayesian CCRB-type lower bound on the missing-mass MSE (mmMSE), named the missing-mass CCRB (mmCCRB), based on the missing-mass unbiasedness. The missing-mass unbiasedness and the proposed mmCCRB can be used to evaluate the performance of existing estimators. Based on the new mmCCRB, we propose a new method to improve existing estimators by an iterative missing-mass Fisher scoring method. Finally, we demonstrate via numerical simulations that the proposed mmCCRB is a valid and informative lower bound on the mmMSE of state-of-the-art estimators for this problem: the CML, the Good-Turing, and Laplace estimators. We also show that the performance of the Laplace estimator is improved by using the new Fisher-scoring method.

A self-learned decomposition and classification model for schizophrenia diagnosis

Background: Schizophrenia (SZ) is a type of neurological disorder that is diagnosed by professional psychiatrists based on interviews and manual screening of patients. The procedures are time-consuming, burdensome, and prone to human error. This urgently necessitates the development of an effective and precise computer-aided design for the detection of SZ. One such efficient source for SZ detection is the electroencephalogram (EEG) signals. Because EEG signals are non-stationary, it is challenging to find representative information in its raw form. Decomposing the signals into multi-modes can provide detailed insight information from it. But the choice of uniform decomposition and hyper-parameters leads to information loss affecting system performance drastically. Method: In this paper, automatic signal decomposition and classification methods are proposed for the detection of SZ and healthy control EEG signals. The Fisher score method is used for the selection of the most discriminant channel. Flexible tunable Q wavelet transform (F-TQWT) is developed for efficient decomposition of EEG signals by reducing root mean square error with grey wolf optimization (GWO) algorithm. Five features are extracted from the adaptively generated subbands and selected by the Kruskal Wallis test. The feature matrix is given as an input to the flexible least square support vector machine (F-LSSVM) classifier. The hyper-parameters and kernel of classifier are selected such that the accuracy of each subband is maximized using GWO algorithm. Results: The effectiveness and superiority of the proposed method is tested by evaluating seven performance parameters. An accuracy of 91.39%, sensitivity, specificity, precision, F-1 measure, false positive rate and error of 92.65%, 93.22%, 95.57%, 0.9306, 6.78% and 8.61% is achieved. The results prove superiority of the developed F-TQWT decomposition and F-LSSVM classifier over existing methodologies. Conclusion: The EEG signals of healthy control and SZ subjects performing motor and auditory tasks simultaneously provide higher discrimination ability over the subjects performing auditory and motory tasks separately. The developed model is accurate, robust, and effective as it is developed on a relatively larger data-set, obtained maximum performance, and tested using ten-fold cross-validation technique. This proposed model is ready to be put to test for real-time SZ detection.

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

Statistical Foundations of Actuarial Learning and its Applications

The aim of this manuscript is to provide the mathematical and statistical foundations of actuarial learning. This is key to most actuarial tasks like insurance pricing, product development, claims reserving and risk management. The basic approach to these tasks is regression modeling. This manuscript describes the exponential dispersion family which is the most commonly used family of distributions in actuarial modeling. It discusses model fitting and parameter estimation using classical tools from mathematical statistics. It then introduces the crucial tools for prediction and forecast evaluation. Based on these statistical concepts various regression models are studied such as generalized linear models, mixture models and neural network regression models. We explore these modeling approaches from a theoretical and a practical viewpoint on publicly available data and we discuss their applications to insurance modeling. This involves model fitting using Fisher's scoring method, gradient descent algorithms or the expectation-maximization algorithm, model selection, parameter selection, regularization, etc.

Accurate Prediction of Gene Mutations with Flow Cytometry Immune-Phenotyping By Machine Learning Algorithm

Introduction Identification of gene mutation status prior treatment has improved our capability in risk stratifying acute myeloid leukemia (AML) patients greatly, but it is really a labor-exhausting work to identify these mutations. In this this study, we hypothesize immunophenotyping could predict gene mutation and we aim to develop machine learning algorithms that could predict the AML gene mutation status with the immunophenotype by clinical flow cytometry. Method Retrospective clinical data of patients with AML, including demographic (age & gender), molecular genetics, cytogenetics as well as flow cytometry (FC) data at National Taiwan University Hospital were collected. A total of 529 newly diagnosed de novo AML from 2009 to 2019 enrolled this study. The median age at diagnosis was 58 years (Table 1). In total, 428 NPM1, 415 FLT3, 331 CEBPA and 338 RUNX1 gene testing results and a total of 529 initial diagnostic FC data from these patients were used in developing the gene mutation prediction models. Each FC data sample contained 100,000 cells acquired on FACSCantoII machine 6 fluorescent channels with multiple fluorescent markers. The markers measured are listed in Table 2. There were 19 combinations of markers and fluorescent channels which were served as feature inputs of the machine learning framework. Our proposed machine learning framework can be divided as a phenotype representation learning paradigm and a classification model. To derive the phenotype representation, we trained a multivariate Gaussian Mixture Model (GMM) on the 19-dimension FC data to capture the training data distribution and characteristics in a probabilistic unsupervised manner. Then, a Fisher-scoring method was used to vectorize each sample as a high dimensional representation via differential computation in terms of the learned GMM parameters. This Fisher vectorization method transformed samples to a high dimensional feature space as phenotype vectors. We performed analysis of variance (ANOVA)-based feature selection on these representations which were finally fed into the support vector machine (SVM) classifier. To alleviate the negative effects of imbalance classes in gene mutation identification tasks, we applied synthetic minority oversampling technique (SMOTE) algorithm which augmented the minority class by interpolating samples near support vectors. We train independent SVM models to detect the occurrences of the four gene mutation. The algorithm is evaluated by randomly divided 5-fold cross validation which separates 80% data for training and 20% for testing. Results This gene mutation rate of this cohort for NPM1, FLT3, CEBPA and RUNX1 were 22.2% (95/428), 25.1% (104/415), 20.2% (67/331), and 13.6% (46/338), respectively. The average accuracies (ACC) of the prediction model performance for NPM1, FLT3, CEBPA and RUNX1 were 82.6%, 76.3%, 84.2% and 84.1%, respectively, whereas the area under the ROC curve (AUC) were 77.9%, 63.4%, 80.7% and 67.7%, respectively (Table. 3). Conclusions We demonstrated the potential of the correlation of recurrent AML gene mutation status with immunophenotype of AML through our preliminary gene mutation prediction model. Further study with larger cohorts followed by external validation are needed to further evaluate the feasibility of using machine learning based algorithm as one of triage tools to support physicians in aggressive AML clinical decision before receiving molecular genetic reports. Disclosures Ko: Roche: Honoraria.

Stochastic Functional Estimates in Longitudinal Models with Interval-Censored Anchoring Events.

Timelines of longitudinal studies are often anchored by specific events. In the absence of fully observed the anchoring event times, the study timeline becomes undefined, and the traditional longitudinal analysis loses its temporal reference. In this paper, we considered an analytical situation where the anchoring events are interval-censored. We demonstrated that by expressing the regression parameter estimators as stochastic functionals of a plug-in estimate of the unknown anchoring event time distribution, the standard longitudinal models could be extended to accommodate the situation of less well-defined timelines. We showed that for a broad class of longitudinal models, the functional parameter estimates are consistent and asymptotically normally distributed with a convergence rate under mild regularity conditions. Applying the developed theory to linear mixed-effects models, we further proposed a hybrid computational procedure that combines the strengths of the Fisher's scoring method and the expectation-expectation (EM) algorithm, for model parameter estimation. We conducted a simulation study to validate the asymptotic properties and to assess the finite sample performance of the proposed method. A real data analysis was used to illustrate the proposed method. The method fills in a gap in the existing longitudinal analysis methodology for data with less well defined timelines.

Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems

Feature selection for mixed and incomplete data in terms of numerical and categorical features with missing values has currently gained considerable attention. The development of the neighborhood rough sets-based feature selection method is an important step in improving classification performance, especially in incomplete data with mixed continuous numerical and categorical features. In this paper, a novel feature selection method based on the neighborhood rough sets using Lebesgue and entropy measures in incomplete neighborhood decision systems is proposed, and the method has the capacity to handle mixed and incomplete datasets; further, it can simultaneously maintain the original classification information. First, a Lebesgue measure based on the neighborhood tolerance class is developed to study the positive region and dependency degree. To thoroughly analyze the uncertainty, noise and incompleteness of incomplete neighborhood decision systems, some neighborhood tolerance entropy-based uncertainty measures are presented based on Lebesgue and entropy measures. Then, by combining an algebraic view with an information view in neighborhood rough sets, the neighborhood tolerance dependency joint entropy is defined in incomplete neighborhood decision systems. Moreover, all the corresponding properties are discussed, and the relationships among these measures are established to meaningfully convey the knowledge essence and investigate the uncertainty of incomplete neighborhood decision systems. Finally, for all high-dimensional datasets, the Fisher score method is used to preliminarily eliminate irrelevant features to significantly reduce the computational complexity, and a heuristic feature selection algorithm is designed to improve the classification performance of mixed and incomplete datasets. Experiments under an instance and fifteen public datasets demonstrate that the proposed feature selection method is effective in selecting the most relevant features, achieving great classification ability for incomplete neighborhood decision systems.

Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification

Gene expression data classification is an important technology for cancer diagnosis in bioinformatics and has been widely researched. Due to the large number of genes and the small sample size in gene expression data, feature selection based on neighborhood rough sets is a key step for improving the performance of gene expression data classification. However, some quantitative measures of feature sets may be nonmonotonic in neighborhood rough sets, and many feature selection methods based on evaluation functions yield high cardinality and low predictive accuracy. Therefore, investigating effective and efficient heuristic reduction algorithms is necessary. In this paper, a novel feature selection method based on neighborhood rough sets using neighborhood entropy-based uncertainty measures for cancer classification from gene expression data is proposed. First, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noise of neighborhood decision systems. Then, to fully reflect the decision-making ability of attributes, the neighborhood credibility and neighborhood coverage degrees are defined and introduced into decision neighborhood entropy and mutual information, which are proven to be nonmonotonic. Moreover, some of the properties and relationships among these measures are derived, which is helpful for understanding the essence of the knowledge content and the uncertainty of neighborhood decision systems. Finally, the Fisher score method is employed to preliminarily eliminate irrelevant genes to significantly reduce complexity, and a heuristic feature selection algorithm with low computational complexity is presented to improve the performance of cancer classification using gene expression data. Experiments on ten gene expression datasets show that our proposed algorithm is indeed efficient and outperforms other related methods in terms of the number of selected genes and the classification accuracy, especially as the size of the genes increases.

A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures.

For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel attribute reduction method using Lebesgue and entropy measures in neighborhood rough sets is proposed, which has the ability of dealing with continuous numerical data whilst maintaining the original classification information. First, Fisher score method is employed to eliminate irrelevant attributes to significantly reduce computation complexity for high-dimensional data sets. Then, Lebesgue measure is introduced into neighborhood rough sets to investigate uncertainty measure. In order to analyze the uncertainty and noisy of neighborhood decision systems well, based on Lebesgue and entropy measures, some neighborhood entropy-based uncertainty measures are presented, and by combining algebra view with information view in neighborhood rough sets, a neighborhood roughness joint entropy is developed in neighborhood decision systems. Moreover, some of their properties are derived and the relationships are established, which help to understand the essence of knowledge and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is designed to improve the classification performance of large-scale complex data. The experimental results under an instance and several public data sets show that the proposed method is very effective for selecting the most relevant attributes with high classification accuracy.

Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

A novel synergistic fibroblast optimization based Kalman estimation model for forecasting time-series data

Evolution of a new computational model for estimating the time-varying data is a highly empirical task in scientific computing theory. Using various evolutionary computation techniques, the identified parameters and functions of the estimation model are optimized to improve the performance of the execution process. The objective of this paper is to introduce a novel Synergistic Fibroblast Optimization (SFO) algorithm in Kalman filter, to develop an optimal estimation model, for forecasting the future state variables of time series data. The proposed model is evaluated using the water samples collected from Ukkadam Periyakulam Lake, Coimbatore, India, where water quality forecasting is done. Fisher score method is applied to choose optimal features subset from the specified high dimensional dataset. Standard performance metrics such as root mean square error (RMSE), mean absolute error (MAE) and regression equation of Sum of Squared Error (SSE) are measured to evaluate the performance of the estimation model, and it is also compared with the actual measurements. Experimental results illustrate that SFO based estimation model produces better promising results than conventional estimating methods.

Automated Imaging System for Pigmented Skin Lesion Diagnosis

Through the study of pigmented skin lesions risk factors, the appearance of malignant melanoma turns the anomalous occurrence of these lesions to annoying sign. The difficulty of differentiation between malignant melanoma and melanocytic naive is the error-bone problem that usually faces the physicians in diagnosis. To think through the hard mission of pigmented skin lesions diagnosis different clinical diagnosis algorithms were proposed such as pattern analysis, ABCD rule of dermoscopy, Menzies method, and 7-points checklist. Computerized monitoring of these algorithms improves the diagnosis of melanoma compared to simple naked-eye of physician during examination. Toward the serious step of melanoma early detection, aiming to reduce melanoma mortality rate, several computerized studies and procedures were proposed. Through this research different approaches with a huge number of features were discussed to point out the best approach or methodology could be followed to accurately diagnose the pigmented skin lesion. This paper proposes automated system for diagnosis of melanoma to provide quantitative and objective evaluation of skin lesion as opposed to visual assessment, which is subjective in nature. Two different data sets were utilized to reduce the effect of qualitative interpretation problem upon accurate diagnosis. Set of clinical images that are acquired from a standard camera while the other set is acquired from a special dermoscopic camera and so named dermoscopic images. System contribution appears in new, complete and different approaches presented for the aim of pigmented skin lesion diagnosis. These approaches result from using large conclusive set of features fed to different classifiers. The three main types of different features extracted from the region of interest are geometric, chromatic, and texture features. Three statistical methods were proposed to select the most significant features that will cause a valuable effect in diagnosis; Fisher score method, t-test, and F-test. The selected features of high-ranking score based on the statistical methods are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. The overall System performance was then measured in regards to Specificity, Sensitivity and Accuracy. According to the different approaches that will be mentioned later the best result was showen by the ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96.25% and 97% for dermoscopic and clinical images respectively.

Fast estimation of diffusion tensors under Rician noise by the EM algorithm

Diffusion tensor imaging (DTI) is widely used to characterize, in vivo, the white matter of the central nerve system (CNS). This biological tissue contains much anatomic, structural and orientational information of fibers in human brain. Spectral data from the displacement distribution of water molecules located in the brain tissue are collected by a magnetic resonance scanner and acquired in the Fourier domain. After the Fourier inversion, the noise distribution is Gaussian in both real and imaginary parts and, as a consequence, the recorded magnitude data are corrupted by Rician noise.Statistical estimation of diffusion leads a non-linear regression problem. In this paper, we present a fast computational method for maximum likelihood estimation (MLE) of diffusivities under the Rician noise model based on the expectation maximization (EM) algorithm. By using data augmentation, we are able to transform a non-linear regression problem into the generalized linear modeling framework, reducing dramatically the computational cost. The Fisher-scoring method is used for achieving fast convergence of the tensor parameter. The new method is implemented and applied using both synthetic and real data in a wide range of b-amplitudes up to 14,000s/mm2. Higher accuracy and precision of the Rician estimates are achieved compared with other log-normal based methods. In addition, we extend the maximum likelihood (ML) framework to the maximum a posteriori (MAP) estimation in DTI under the aforementioned scheme by specifying the priors. We will describe how close numerically are the estimators of model parameters obtained through MLE and MAP estimation.

Pigmented Skin Lesion Diagnosis by Automated Imaging System

Pigmented skin lesions are the normal part of the skin, however its anomalous appearance is an annoying sign due to the presence of melanomas one of its malignant forms. Although melanoma is a deadly considerable disease, its early detection is a serious step toward mortality reduction. The proposed research discusses the outcome of introducing312 different features in a non-invasive diagnosis method for pigmented skin lesion diagnosis. To obviate the problem of qualitative interpretation, two different image sets are utilized to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. System contribution appears in using large conclusive set of features fed to different classifiers composing totally complete, new and different approaches for the purpose of disease diagnosis. Miscellaneous types of features used such as geometric, chromatic, and texture features extracted from the region of interest resulted from segmentation process. Then, the most prominent features that can cause an effect are selected by three different methods; Fisher score method, t-test, and F-test. The high-ranking features are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. System performance was measured in regards Specificity, Sensitivity and Accuracy. The ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96. 25% and 97% for dermoscopic and clinical images respectively.

Diagnostics for a Linear Model with First-Order Autoregressive Symmetrical Errors

In this article, we focus on some diagnostics for linear regression model with first-order autoregressive and symmetrical errors. The symmetrical class includes both light- and heavy-tailed univariate symmetrical distributions, which offers a more flexible framework for modeling. Maximum likelihood estimates are computed via the Fisher-score method. Score statistic and its adjustment are proposed for testing autocorrelation of the random errors. Local influence diagnostics are also derived for the model under some usual perturbation schemes. The performances of the test statistics are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our diagnostic methods.

Association Test for X-Linked QTL in Family-Based Designs

Family-based association methods for detecting quantitative trait loci (QTL) have been developed primarily for autosomes, and comparable methods for X-linked QTL have received less attention. We have developed a family-based association test for quantitative traits, named XQTL, which uses X-linked markers in a nuclear family design. XQTL adopts the framework of the orthogonal model implemented in the QTDT program, modifying the sex-specific score for X-linked genotypes. XQTL also takes into account the dosage effect due to female X chromosome inactivation. Restricted maximum likelihood (REML) and Fisher's scoring method are used to estimate variance components of random effects. Fixed effects, derived from the phenotypic differences among and within families, are estimated by the least-squares method. Our proposed XQTL can perform allelic and two-locus haplotypic association tests and can provide estimates of additive genetic effects and variance components. Simulation studies show correct type I error rates under the null hypothesis and robust statistical power under alternative scenarios. The loss of power observed when parental genotypes are missing can be compensated by an increase of offspring number. By treating age at onset of Parkinson disease as a quantitative trait, we illustrate our method, using MAO polymorphisms in 780 families.