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تهدف هذه الدراسة إلى معرفة أثر استخدام أسلوب الانحدار اللوجستي كأحد أساليب التنقيب في البيانات على دعم الرأي المهني لمراجعي الحسابات حول مدى قدرة المنشأة على الاستمرارية ووجود تحريفات جوهرية في القوائم المالية وقد استخدم الباحث أسلوب الدراسة التطبيقية الشركات المدرجة في بورصة الأوراق المالية المصرية وقد توصلت الدراسة إلى مجموعة من النتائج أهمها يؤثر استخدام الانحدار اللوجستي كأحد أساليب التنقيب في البيانات في دعم الرأي المهني لمراجع الحسابات حول وجود تحريفات جوهرية في القوائم المالية كما يؤثر دعم الرأي المهني لمراجع الحسابات حول استمرارية المنشأة، كما توصلت الدراسة إلى ضرورة توسـيع نطـاق الإجـراءات التحليلية لتتضمن أساليب التنقيب في البيانات المناسبة لتحسين دقة التنبؤ بقدرة المنشأة على الاستمرارية وبمدى وجود تحريفات جوهرية في القوائم المالية.

In this article, we study a new extension of the log-logistic model called the Kumaraswamy alpha-power log-logistic (KAPLL) distribution, an extension of the log-logistic model. It investigates some of their mathematical and statistical properties, including reliability properties (survival function, hazard rate function (HRF), moments, quantile functions (QF), and moment generating functions), emphasizing their utility in modeling diverse aging and failure criteria. One key advantage of the KAPLL distribution lies in its capacity to represent its density as a blend of log-logistic densities, offering both symmetric and asymmetric shapes for greater modeling flexibility. The estimation of KAPLL parameters is achieved through maximum likelihood estimation (MLE), a widely used statistical method. The study presents comprehensive simulation results to assess the effectiveness of the proposed estimation technique. Furthermore, a practical application on real-world data is conducted to showcase the adaptability and versatility of the KAPLL distribution when compared with other extensions of the log-logistic model.

This paper proposes a new estimator based on the singular value decomposition technique of the design matrix to remedy multicollinearity in the binary logistic model. The proposed estimator is called the SVD-based maximum likelihood logistic estimator. The theoretical properties of this estimator and its superiority over some existing estimators is derived in the sense of the matrix mean squared error criterion. The choice of scalar parameter for this estimator is discussed. A Monte Carlo simulation study has been conducted to compare the performance of the proposed estimator with the existing maximum likelihood estimator and ridge logistic estimator in terms of the mean squared error criterion. Moreover, a real data application is presented to illustrate the potential benefits of the proposed estimator and satisfy the theoretical findings. The results from the simulation study and the empirical application reveal that the proposed estimator works well and outperforms existing estimators in scalar mean squared error sense.

The binary logistic regression (BLR) model is used as an alternative to the commonly used linear regression model when the response variable is binary. As in the linear regression model, there can be a relationship between the predictor variables in a BLR, especially when they are continuous, thus giving rise to the problem of multicollinearity. The efficiency of maximum likelihood estimator (MLE) is low in estimating the parameters of BLR when there is multicollinearity alternatively, the ridge estimator (RR), the Liu estimator (LE), the Liu-type estimator (LTE) and The Modified Ridge-Type estimator (MRTE) were developed to replace MLE. However, in this study, we compared all estimators by the mean squares errors (MSE) to get the best estimator that mitigates the effect of multicollinearity. Finally, a simulation study was conducted to illustrate the theoretical results. The result shows that the modified Ridge type estimator outperforms all other estimators followed by Liu estimator.

This paper estimates effects of Algerian financial liberalization policy on possibility of bank crises using a logistic regression (logit) model (1970- 2018). We find that financial liberalization would have no significant and immediate impact on crisis. Results show that inflation rate, exchange rate, and fuel exports have a very significant impact on occurrence of crises in Algeria. On other hand, internal liberalization by granting loans or external financial liberalization through liberalization of financial market and capital account has no impact on crisis.

استهدف البحث دراسة مقارنة بين أسلوب الشبكات العصبية الاصطناعية ونموذج انحدار كوكس ونموذج الانحدار اللوجستي وذلك بالتطبيق على عينة من 161 مريض سرطان للرئة ومجموعة من العوامل المؤثرة في زمن البقاء على قيد الحياة وتوصلت الدراسة إلى أن باستخدام أسلوب الشبكات العصبية الاصطناعية أهم العوامل المؤثرة في زمن البقاء هي التدخين والمهنة ودرجة المرض ثم العمر بكفاءة تصل إلى 86% وحساسية 85% ونوعية 87% ونسبة تصنيف خاطئ 14% أما نموذج انحدار كوكس توصل إلى أن أهم العوامل المؤثرة في زمن البقاء وهي درجة المرض، وطرق العلاج، الإصابة بكورنا والتدخين وذلك بكفاءة 80.7% وحساسية 82.5% ونوعية 79% ونسبة تصنيف خاطئ 19.8% أما باستخدام نموذج الانحدار اللوجستي فأهم العوامل المؤثرة في زمن البقاء هي المهنة، ودرجة المرض، التدخين ثم العمر بكفاءة تصل إلى 67.1% وحساسية 54.3% ونوعية 76.9% ونسبة تصنيف خاطئ 32.9% وبالتالي هناك أفضلية للشبكات العصبية الاصطناعية عن نموذج انحدار كوكس ونموذج الانحدار اللوجستي في تحديد العوامل المؤثرة في زمن البقاء والتصنيف والتنبؤ بالمشاهدات الجديدة

In competing risks data, when a person experiences more than one event in the study, usually the probability of experiencing the event of interest is altered. Therefore, it is necessary to analyze the competing risk data. Objectives: The current study aimed at analyzing the colorectal cancer (CRC) risk factors based on competing risks model. The loglogistic model was also fitted with 2-parameter to evaluate the prognostic factors that affect the survival of patients with CRC, and comparisons were made to find the best model. Methods: The current retrospective study was conducted on 1054 patients with CRC registered in the Research Institute of gastroenterology and liver disease center (from 2004 to 2015), Taleghani hospital, Tehran, Iran. The demographic and clinical features including age at diagnosis, gender, family history of CRC, body mass index (BMI), tumor size, and tumor site were extracted from the hospital documents. Analysis was performed using competing risks model and was based on the 4-parameter log-logistic distribution and log-logistic distribution. The analysis was carried out using R software version 3.0.3. P value less than 0.05 was considered as significant. Results: Overall, 1054 patients with CRC and complete data were included in the analysis. The mean ± standard deviation (SD) of survival time was 92 ± 6.62 months. Out of the 1054 patients, 379 (36%) subjects died of CRC and 49 (4.6%) subjects died of other causes such as myocardial infarction, stomach cancer, liver cancer, etc. Four-parameter log-logistic model and log-logistic model with competing risk analysis indicated age at diagnosis and BMI as the prognosis. Conclusions: The current study indicated age and BMI as prognosis of CRC, using a 4-parameter log-logistic model with competing risk analysis. Although the odds ratio (OR) in 4-parameter log-logistic model and log-logistic model ones were approximately similar, according to Akaike information criterion, the 4-parameter log-logistic model was more appropriate for survival analysis.

Gene expression regulation is a vital process in the body to ensure that cells produce the correct amount of proteins when they need them. Any disruption to this regulation can lead to serious consequences, including cancer). miRNAs are micro molecules that control gene expression by targeting a mRNA and binding to specific sites within the 3'UTR or the 5'UTR and increase or decrease gene expression. Hence, it's crucial to predict gene regulatory response in order to be able to control it. Two of the most widely used statistical methods for analyzing categorical outcome variables are LDA and logistic regression. While both are appropriate for the development of linear classification models, i.e. models associated with linear boundaries between the groups. Nevertheless, the two methods differ in their basic idea. LDA makes more assumptions about the underlying data. It is therefore reasonable to expect LDA to give better results in the case when the normality assumptions are fulfilled, but in all other situations LR should be more appropriate. However, in practice, the assumptions are nearly always violated; therefore, we try to check the performance of both methods with simulations. Previously (In our last paper) we have studied gene regulatory mechanisms using Logistic Regression. In this paper, we present a simulation study between Logistic Regression and LDA in the prediction of gene regulatory response.

يعد مرض السكري من أكثر الأمراض شيوعا ومن أكثرها خطر إذ يترتب على هذا المرض الكثير من المضاعفات التي تؤدي إلى الوفاة. يهدف البحث إلى بناء نموذج انحدار لوجستي للإصابة بمرض السكري من النوعين الأول والثاني، وقد اعتمد البحث على بيانات أوليه مأخوذة من عينة بحجم (216) مريضا بالسكري من كلا الجنسين وبفئات عمرية مختلفة من مستشفى البصرة العام. استخدمت الباحثة المنهجين الوصفي والتحليلي إذ تم بناء نموذج الانحدار اللوجستي الثنائي ثم تحليل البيانات باستخدام برنامج SPSS ومن أهم النتائج التي أظهرتها الدراسة أن نموذج الانحدار اللوجستي الثنائي المستخدم له قدرة تفسيرية وتصنيفية عالية وبشكل دال إحصائيا. نجحت المتغيرات المستقلة الداخلة في النموذج في تفسير ما نسبته (45%) إلى (77%) من التغيرات في المتغير التابع وهذا ما أكدته قيم R2 ولكن بحسب هذه النتيجة توجد عوامل أخرى لم تدرج في النموذج، قد يكون لها تأثير وهذا العوامل قد تكون نفسية أو وراثية وإن نموذج متغيرات الدراسة أظهر تأثيرا معنويا لكل من (العمر، الوزن) على تصنيف المريض سواء كان مصابا بالسكري من النوع الأول والثاني إذ بلغت نسبة التصنيف الإجمالي الصحيح في النموذج (94%) والتصنيف الصحيح بمرض السكري من النوع الأول (75%) أما المصابين بمرض السكري من النوع الثاني فأن نسبة التصنيف الصحيح لهم (98%).

This paper aimed to compare between the two different methods of classification: linear discriminant analysis (LDA) and multinomial logistic regression (MLR) using the overall classification accuracy, investigating their quality of prediction in terms of sensitivity and specificity, and examining area under the ROC curve (AUC) in order to make the choice between the two methods easier, and to understand how the two models behave under different data and group characteristics. Model performance had been assessed from two special cases of the kfold partitioning technique, the 'leave-one-out' and 'hold out' procedures. The performance evaluation for the two methods was carried out using real data and also by simulation. Results show that logistic regression slightly exceeds linear discriminant analysis in the correct classification rate, but when taking into account sensitivity, specificity and AUC, the differences in the AUC were negligible. By simulation, we examined the impact of changes regarding the sample size, distance between group means, categorization, and correlation matrices between the predictors on the performance of each method. Results indicate that the variation in sample size, values of Euclidean distance, different number of categories have similar impact on the result for the two methods, and both methods LDA and MLR show a significant improvement in classification accuracy in the absence of multicollinearity among the explanatory variables.