A stochastic process {Xk, k = 1, 2, ...} is a doubly geometric stochastic process if there exists the ratio (a > 0) and the positive function (h(k) > 0), so that {α 1 h-k }; k ak X k = 1, 2, ... is a generalization of a geometric stochastic process. This process is stochastically monotone and can be used to model a point process with multiple trends. In this paper, we use nonparametric methods to investigate statistical inference for doubly geometric stochastic processes. A graphical technique for determining whether a process is in agreement with a doubly geometric stochastic process is proposed. Further, we can estimate the parameters a, b, μ and σ2 of the doubly geometric stochastic process by using the least squares estimate for Xk a

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most

Gumbel distribution was dealt with great care by researchers and statisticians. There are traditional methods to estimate two parameters of Gumbel distribution known as Maximum Likelihood, the Method of Moments and recently the method of re-sampling called (Jackknife). However, these methods suffer from some mathematical difficulties in solving them analytically. Accordingly, there are other non-traditional methods, like the principle of the nearest neighbors, used in computer science especially, artificial intelligence algorithms, including the genetic algorithm, the artificial neural network algorithm, and others that may to be classified as meta-heuristic methods. Moreover, this principle of nearest neighbors has useful statistical featu

The general aim of an experimental design in this paper was to estimate the different treatments effects on the responses by statistical methods. The estimates must be averting biases and the random errors minimized as much as possible. We used multivariate analysis of variance (MANOVA) to analyze design of experiments for several responses. In this paper, we provided three fertilizers (mineral, humic, micro-elements) applied on Yellow Maize experiment. This experiment was conducted by completely randomized design (CRD). We tested four responses (Chlorophyll in paper, total ton / ha, paper area / cm2 and plant height / cm) together to find significant test between them. The partial correlations are between Chlorophyll in paper and total ton

             In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models  precisely. In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming. Non-parametric methods can also used to assume the parametric regression model for subsequent use. Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data. This problem can be solved by using kernel ridge regression which depend o

Use of lower squares and restricted boxes
In the estimation of the first-order self-regression parameter
AR (1) (simulation study)

Theresearch took the spatial autoregressive model: SAR and spatial error model: SEM in an attempt to provide a practical evident that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial andthat includes all of them spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. Spatial analysis had

In this research we been estimated the survival function for data suffer from the disturbances and confusion of Iraq Household Socio-Economic Survey: IHSES II 2012 , to data from a five-year age groups follow the distribution of the Generalized Gamma: GG. It had been used two methods for the purposes of estimating and fitting which is the way the Principle of Maximizing Entropy: POME, and method of booting to nonparametric smoothing function for Kernel, to overcome the mathematical problems plaguing integrals contained in this distribution in particular of the integration of the incomplete gamma function, along with the use of traditional way in which is the Maximum Likelihood: ML. Where the comparison on the basis of the method of the Cen

Time series have gained great importance and have been applied in a manner in the economic, financial, health and social fields and used in the analysis through studying the changes and forecasting the future of the phenomenon. One of the most important models of the black box is the "ARMAX" model, which is a mixed model consisting of self-regression with moving averages with external inputs. It consists of several stages, namely determining the rank of the model and the process of estimating the parameters of the model and then the prediction process to know the amount of compensation granted to workers in the future in order to fulfil the future obligations of the Fund. , And using the regular least squares method and the frequ

In this article we study the variance estimator for the normal distribution when the mean is un known depend of the cumulative function between unbiased estimator and Bays estimator for the variance of normal distribution which is used include Double Stage Shrunken estimator to obtain higher efficiency for the variance estimator of normal distribution when the mean is unknown by using small volume equal volume of two sample .

              يتكون الانحدار المقسم من عدة أقسام تفصل بينها نقاط انتماء مختلفة، فتظهر حالة عدم التجانس الناشئة من عملية فصل الأقسام ضمن عينة البحث. ويهتم هذا البحث في تقدير موقع نقطة التغيير بين الأقسام وتقدير معلمات الأنموذج، واقتراح طريقة تقدير حصينة ومقارنتها مع بعض الطرائق المستعملة في الانحدار الخطي المقسم. وقد تم استعمال أحد الطرائق التقليدية (طريقة Muggeo) لإيجاد مقدرات الإمكان الأعظم بالأسلوب الت

Circular data (circular sightings) are periodic data and are measured on the unit's circle by radian or grades. They are fundamentally different from those linear data compatible with the mathematical representation of the usual linear regression model due to their cyclical nature. Circular data originate in a wide variety of fields of scientific, medical, economic and social life. One of the most important statistical methods that represents this data, and there are several methods of estimating angular regression, including teachers and non-educationalists, so the letter included the use of three models of angular regression, two of which are teaching models and one of which is a model of educators. ) (DM) (MLE) and circular shrinkage mod

المستخلص ان عملية تقدير الانموذج وأختيار المتغير المعنوي هي عملية حاسمة في النمذجة شبه المعلميه semi-parametric modeling)) ففي بدايه عملية النمذجة كثيرا" ما يكون هنالك عدد كبير من المتغيرات التوضيحية لتجنب فقدان أي عناصر تفسيريه قد تكون هامة ونتيجة لذلك فأن أختيار المتغيرات المعنوية أصبحت ضرورة فضلاً عن ان عملية أختيار المتغير ليس الغرض منه تبسيط الأنموذج المعقد وتفسيره فقط ولكن كذلك القدرة على التنبؤ . في هذا ا

زاد اهتمام الباحثين في السنوات الاخيرة بنماذج الانحدار شبه المعلمية وذلك اذ يمكن من خلالها دمج نماذج الانحدار اللامعلمي والمعلمي في ان واحد ومن ثم تكوين انموذج انحدار يتميز بامكانية التعامل مع مشكلة تعدد الابعاد الموجودة في النماذج اللامعلمية والتي تحصل عند زيادة عدد المتغيرات التوضيحية الداخلة في التحليل و من ثم تناقص دقة التقدير.  فضلا عن امتياز هذا النوع من النماذج بمرونة في المجال التطبيقي مقارنةً

في هذا البحث تمت دراسة احد نماذج العمليات  العشوائيه التصادفية وهو احد نماذج   le'vyمعتمدين على مايسمى بالحركه البراونيه ذي الأحداثيات الجزئية Brownia subordinate. اذ تم الاعتماد على ما يسمى بأنموذج معكوس كاوس الطبيعي  Normal Inverse Gassian (NIG اذ يهدف هذا البحث الى تقدير معالم ذلك الأنموذج بأستعمال طريقتي العزوم والأمكان الأعظم , ومن ثم توظيف تلك المقدرات للمعالم في دراسة عوائد الأسهم وتقييم اصول التسعير للمصرف المتحد ومصر

المستخلص : في هذا البحث تم تقدير انموذج دالة التحويل في السلاسل الزمنية بأستعمال طرائق مختلفة منها معلميه  متمثلة بطريقة طريقة دالة الترجيح الشرطية Conditional Likelihood Function فضلاً عن استعمال مقدرات لا معلميه تتمثل بطريقتين الانحدار الخطي الموضعي Local Linear Regression وطريقة الشريحة التمهيدية التكعيبية cubic smoothing spline والهدف من هذا البحث هو مقارنة تلك المقدرات مع انموذج دالة التحويل اللاخطية المعلمية باستعمال اسلوب ال

يهدف هذا البحث الى تقدير الانموذج الخطي الجزئي (Partial Linear Regression Model) بإستعمال طريقتين من طرائق التمهيد وهما طريقتي التمهيد المويجي (Wavelet Smoother)  والتمهيد اللبي (Kernel Smoother). تم استعمال تجارب المحاكات لبيان افضل تلك الطرائق في تقدير هذا النوع من النماذج بإختلاف الحالات والدوال وحجوم العينات والتباينات المستعملة. تم الاعتماد على معيار معدل متوسط مربعات الخطأ (Mean Average Squares Error) كأحد معايير المقارنة بين الطرائق. واثب

ان هدف هذا البحث يتمثل بيان أهمية تقدير دالة الانحدار شبه المعلميه .إذ تم استعراض بعض الطرائق شبه المعلميه مع بعض الطرائق المقترحة لتقدير دالة الانحدار , ومن ثم مقارنة هذه الطرائق من خلال أسلوب المحاكاة باستعمال توزيعات, إحجام عينات ومستويات تباين مختلفة للمتغير X , اذ تم اقتراح مقدر مدمج شبه معلمي لتقدير دالة الانحدار وقد اثبتت النتائج افضلية هذا المقدر المقترح في حالة الأنموذجين الأول والثاني, إما للأنم

In this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method  and the least squares method and that using the method of simulation model  first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.

لقد لاقت مواضيع استعمال التحليل الاحصاء المالي رواجاً كبيراً في الآونة الأخيرة سواء على مستوى الافراد والشركات العامة والخاصة مروراً بأسواق الاسهم والاوراق المالية (البورصات) وصولاً الى اقتصاديات الدول والبلدان. وذلك بعد وصول الباحثين والدارسين للظواهر الاقتصادية والمالية بكل أنماطها الى إدراك أهمية التحليل الكميّ عموماً والتحليل الاحصائي على وجه الخصوص، مما دفعنا لتأليف الطبعة الأولى من هذا الكتاب بالع

ان الهدف من الدراسة هو بيان القدرة التنبؤية الافضل بين انموذج الانحدار اللوجستي والدالة المميزة الخطية باستعمال البيانات الاصليه اولا ثم المركبات الرئيسة لتقليص الابعاد بين المتغيرات لبيانات المسح الاجتماعي والاقتصادي للاسرة لمحافظة بغداد لعام 2012 وتضمنت عينة البحث 615 مفردة لـ13 متغير، 12منها متغير توضيحي والمتغير المعتمد شمل العاملين والعاطلين عن العمل، تم اجراء المقارنة بين الطريقتين اعلاه واتضح من خلال

Semiparametric methods combined parametric methods and nonparametric methods ,it is important in most of studies which take in it's nature more progress in the procedure of accurate statistical analysis which aim getting estimators efficient, the partial linear regression model is considered the most popular type of semiparametric models, which consisted of parametric component and nonparametric component in order to estimate the parametric component that have certain properties depend on the assumptions concerning the parametric component, where the absence of assumptions, parametric component will have several problems for example multicollinearity means (explanatory variables are interrelated to each other) , To treat this problem we use

The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift