In the realm of economic (financial) time series analysis, accurate prediction holds paramount importance. However, these data often suffer from the presence of noise, particularly in highly random and non-stationary datasets like stock market data. Dealing with noisy data makes predicting noise-free economic models exceedingly challenging. This research paper introduces an innovative shrinkage (thresholding) function designed to improve the efficiency of wavelet shrinkage denoising in the context of financial time series data. The proposed function is constructed based on an arctangent model with adjustable parameters meticulously chosen to ensure the function maintains continuous differentiability. The application of this novel shrinkage function effectively reduces noise in stock data. Employing R program for data analysis and figure plotting, the performance of this approach is rigorously validated using closing price data from the Shanghai Composite Index, spanning the period from January 4, 2000 to August 28, 2023. The experimental results demonstrate that the proposed thresholding function outperforms classical shrinkage functions (hard, soft, and nonnegative garrote) in both continuous derivative property and denoising efficacy.

The focus of this research is to examine the impact of corporate social responsibility (CSR) on consumers' perceptions. A study has been conducted to investigate customers' perspectives to achieve the purpose of enhancing comprehension of the diverse elements of CSR, which include Philanthropic, Economic, Environmental, Ethical, and Legal concerns. In addition to conducting a comparative analysis with the findings of the present study, this research will also examine and compare its results with a selection of social responsibility studies conducted in different countries. A total of 75 individuals, spanning an age range from under 20 to over 30, were requested to respond to a questionnaire to collect data. Among the total population, it was observed that the majority consisted of women, accounting for 56%, while men constituted 44% of the population. The Duhok Polytechnic University (DPU) Technical College of Administration was the research community. A representative random sample comprising undergraduate students, employees, and lecturers was selected. The present study contributes to the existing body of knowledge on corporate social responsibility in the Iraq, Kurdistan Region, specifically focusing on the perspective of consumers. The findings of this study indicate that the participants in the study exhibited a preference for ethical duty, assigning it a greater level of importance compared to legal responsibility and economic responsibility. KEYWORD:

In this study, a novel generalized New Alpha Inverse Weibull (NAPIW) distribution is defined utilizing the Alpha power transformation method. Its statistical features, such as reliability and moments, are obtained. Additionally, the estimate of the NAPIW parameters using the maximum likelihood estimation approach is discussed. Eventually, the proposed new distribution is applied to actual data reflecting the time spent waiting for customer assistance in a bank, and its goodness-of-fit is shown. Furthermore, we use simulation data as well. Moreover, calculating mean square errors and bias for all parameters and some other measurements as well.

Accelerated life testing is a fundamental practice in reliability engineering, allowing the evaluation of component or device performance over extended lifetimes impractical to encounter during design. This study delves into the application of the transmuted Weibull distribution to model lifetime data, showcasing its versatility in real-world scenarios. The evaluation includes critical metrics such as Akaike’s information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination, and standard error for distribution comparison. Utilizing Maximum Likelihood Estimation (MLE) for parameter estimation, a simulation study is conducted with varying sample sizes, and the R programming language is employed for in-depth analysis. Real data analysis involves comparing the Transmuted Weibull (twd) model with other models using goodness-of-fit criteria. Maximum Likelihood Estimates (MLEs) are obtained, and the likelihood ratio test demonstrates the (twd) model's superior alignment with the data. The study concludes with the simplicity of producing Quick Fit plots for analysis using R software. The presented approach provides a comprehensive understanding of reliability characteristics, combining theoretical insights with practical applications and numerical analyses

Abstract: Objective: This research aims to compare two nonparametric functional regression models, the Kernel Model and the K-Nearest Neighbor (KNN) Model, with a focus on predicting scalar responses from functional covariates. Two semi-metrics, one based on second derivatives and the other on Functional Principle Component Analysis, are employed for prediction. The study assesses the accuracy of these models by computing Mean Square Errors (MSE) and provides practical applications for illustration. Method: The study delves into the realm of nonparametric functional regression, where the response variable (Y) is scalar, and the covariate variable (x) is a function. The Kernel Model, known as funopare.kernel.cv, and the KNN Model, termed funopare.knn.gcv, are used for prediction. The Kernel Model employs automatic bandwidth selection via Cross-Validation, while the KNN Model employs a global smoothing parameter. The performance of both models is evaluated using MSE, considering two different semi-metrics. Results: The results indicate that the KNN Model outperforms the Kernel Model in terms of prediction accuracy, as supported by the computed MSE. The choice of semi-metric, whether based on second derivatives or Functional Principle Component Analysis, impacts the model's performance. Two real-world applications, Spectrometric Data for predicting fat content and Canadian Weather Station data for predicting precipitation, demonstrate the practicality and utility of the models. Conclusion: This research provides valuable insights into nonparametric functional regression methods for predicting scalar responses from functional covariates. The KNN Model, when compared to the Kernel Model, offers superior predictive performance. The selection of an appropriate semi-metric is essential for model accuracy. Future research may explore the extension of these models to cases involving multivariate responses and consider interactions between response components.

This research investigates the significance of accounting treatments for brand values. The focus of this study was on well-known Chinese companies with stock trading on the NASDAQ Stock Market, the Shanghai Stock Exchange, the Shenzhen Stock Exchange, and the Hong Kong Stock Exchange. In the WPP and Interbrand brand ranking reports, the selected listed companies are specifically listed. Only 69 businesses were selected for this study, even though each of the two WPP and Interbrand ranking lists contains 50 businesses. To determine the relationships between a company's brand value, book value, and market value, a comparative analysis is performed. The research discovered that a company's book value and market value are highly correlated. Market value is also related to brand value. However, the brand value of a company is not perfectly correlated with its book value. Because IAS 38 prohibits recognizing internally generated brands on financial statements, it demonstrated that most companies did not consider the brand value

Outliers are data points or observations that stand out significantly from the rest of the group in terms of size or frequency. They are also referred to as "abnormal data". Before fitting a forecasting model, outliers are often eliminated from the data set, or if not removed, the forecasting model is altered to account for the presence of outliers. The first scenario covered in the study is the detection of outliers when the parameters have been established. Second, where there are unidentified parameters. This article mentions a number of causes for outlier correction and detection in time series analysis and forecasting. For the objective of the study, a time series of the volume of water entering the Dohuk dam reservoir in Dohuk city was used. The study arrived at the following conclusions after conducting their research: first, whenever the critical value increased, the value of residual standard error (with outlier adjustment) increased. Second, the quantity of outlier values dropped each time the critical value was raised. Third, forecasts with outlier correction perform better than forecasts without outlier adjustment when outliers are present.

Managers and researchers are paying more and more attention to corporate social responsibility (CSR), especially in the area of consumer perception and response to CSR. The purpose of this study is to determine how Iraqi consumers in the Kurdistan Region perceive corporate social responsibility, as well as the importance of philanthropic, economic, environmental, ethical, and legal aspects of corporate social responsibility. The study will also compare the findings to those of numerous studies on social responsibility conducted in various countries with the current study. A questionnaire was designed to collect data from 75 participants, including (56%) women and (44%) men ranging in age from (Less than 20) to (More than 30). An appropriate random sample of undergraduate students, staff, and lecturers from Duhok Polytechnic University's Technical College of Administration was taken as a research community. Then comparing the finding of the study with other studies from different countries. The research presented in this paper improves our comprehension of corporate social responsibility from the consumer’s perspective in the Kurdistan Region of Iraq. The finding of this study demonstrates that the importance of ethical responsibility was prioritized by the study sample, with economic responsibility coming in second and legal responsibility coming third.

Purpose: The Purpose of the study was to examine the validity of the Altman Z- Score and Sherrod Z- Score models in financial failure prediction. To achieve the study's goal, references from various authors who have reviewed this topic were used. Theoretical framework: The study highlights the importance of analyzing and delving into the various notions of financial failure and distress. When it comes to potential effects on the wealth of creditors, stockholders, and society as a whole, academics and researchers consider a company's distress and bankruptcy to be the most important issue to be studied. In order to maintain the goal of company survival and continuity before the disaster happens, many academics started looking for a method to identify and forecast distress and failure. Design/methodology/approach: Altman Z-score and Sherrod Z- score employed a multi-discriminant model to predict the financial position of ten ISE banks between 2009 - 2013. Z- Score models from Altman and Sherrod were used to determine whether the banks listed on the ISE are exposed to failing financially. Ten banks out of the forty - six banks listed on the ISE were selected. The study only used secondary data obtained from the chosen banks' financial statements in ISE. Findings: Based on Altman's Z- score model, the study examines that certain banks are particularly exposed to failure. In contrast, the Sherrod Z- Score model indicates that the chosen banks have some issues, but they are minor, and the risk of bankruptcy is low. Research, scientific and social implications: By using a failure prediction model, it is possible to determine the likelihood that banks will experience financial failure in the future. Investors could use this information to guide their decision-making going forward. Originality/value: The value and importance of research related to the study of financial failure prediction models in Iraqi commercial banks. The research also seeks to explain financial failure models and the extent to which investors benefit from these models.

This paper proposed a new method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA)) for measureing the closeness between curves. Root Mean Square Errors is used for the implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables model, results are more preferable than the independent response method. The models are demonstrated by both a simulation data and real data.

Abstract: Except for its significance and relevance in the performance evaluation of organizations, the research emphasizes the need of studying and analyzing the many concepts of financial distress and failure. The distress and bankruptcy of a firm is regarded as the most significant issue researched by academics and researchers in terms of the possible effects on the wealth of creditors, stockholders, and society. As a result, many academics began to hunt for a strategy to identify and forecast distress and failure in order to retain the aim of company survival and continuity before the tragedy occurs. The purpose of this study was to evaluate the applicability of the B-Sherrod model, which was deemed an advanced model for detecting this phenomenon, on a sample of banks from an Iraqi stock market from 2009 to 2015. The study indicated that the B-Sherrod Model for recognizing and forecasting economic difficulties should be used as a realistic tool for evaluating a company's performance. Empirical test findings demonstrated the Sherrod model's usefulness in detecting financial distress, which will assist investors and other concerned users in visualizing a company's capacity to continue operations.

Nonparametric functional regression is of considerable importance due to its impact on the development of data analysis in a number of fields, least cost and saving time. In this thesis, we focus on nonparametric functional regression and its extensions, and its application to functional data. We first review nonparametric functional regression, followed by a detailed discussion about model structures, semi-metrics and kernel functions. Secondly, we extend the independent response model to multivariate response variables with functional covariates. Our model uses the K-Nearest Neighbour function with automatic bandwidth selection by a cross-validation procedure, and where the closeness between functional data is measured via semi-metrics. Then, in the third topic, we use the principal component analysis to decorrelate multivariate response variables. After that, in the fourth topic, we add new results to the nonparametric functional regression when the covariate is functional and the response is multivariate in nature with different bandwidths for different responses, and where the correlation among different responses is taken into account with different bandwidths for different responses. Our model uses the kernel function with automatic bandwidth selection via a cross-validation procedure and semi-metrics as a measure of the proximity between functional data. Finally, we extend the univariate functional responses to the multivariate case and then take the correlation between different functional responses into account. The e ectiveness of the proposed models is illustrated through simulated instances. The proposed methods are then applied to …

Nonparametric regression methods have been widely studied in functional regression analysis in the context of functional covariates and univariate response, but it is not the case for functional covariates with multivariate response. In this paper, we present two new solutions for the latter problem: the first is to directly extend the nonparametric method for univariate response to multivariate response, and in the second, the correlation among different responses is incorporated into the model. The asymptotic properties of the estimators are studied, and the effectiveness of the proposed methods is demonstrated through several simulation studies and a real data example.

Nonparametric regression methods have been widely studied in functional regression analysis in the context of functional covariates and univariate response, but it is not the case for functional covariates with multivariate response. In this paper, we present two new solutions for the latter problem: the first is to directly extend the nonparametric method for univariate response to multivariate response, and in the second, the correlation among different responses is incorporated into the model. The asymptotic properties of the estimators are studied, and the effectiveness of the proposed methods is demonstrated through several simulation studies and a real data example.

The stepwise regression and other method to know the best method when the model contains intercept and without intercept, and applying the method leverage point when we added the new point to the original data. We testing the significant intercept by using (F,AIC and Cp) test.