مجید سرمد

  Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first synoptic station from 1330 (1951) is available at the Iranian Meteorological Organization website The historical monthly precipitation and temperature of five stations in Iran is available since 1880 with missing data. These data measured by the Embassy of the United States and Britain from the Qajar period and recorded in World Weather records books. These synoptic stations include Mashhad, Isfahan, Tehran, Bushehr, and Jask. The monthly missing data were predominantly recorded during World War II (1941-1949). Unfortunately, these data have missing. Therefore, the accuracy of simulating these variables is very important.  The current research aimed to predict the missing values of monthly temperature and precipitation in Mashhad station. The stations in the neighboring countries were selected due to the distance to Mashhad, relationship, and completeness of data since 1880, as the predictive variables. Monthly precipitation of Ashgabat from Tajikistan and Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as an independent variable in the making of Missing Rainfall in Mashhad. Also, the temperature of Ashgabat, Bayram Ali, Gudan, Sarakhs, and Tajan were selected to restore the monthly temperature of the Mashhad station. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and has fitted 6 multiple regression to the monthly temperature of Mashhad. then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. Also, the Artificial Neural Network (MLP) model and Support vector regression have been selected and implemented in order to simulate monthly precipitation and temperature data of Mashhad. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection. Ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). At the first stage, several multiple regressions were fitted to monthly precipitation (with coefficients ranging from 0.63 to 0.81) and six patterns for monthly temperature (0.986-0.993). Afterward, GA and ACO were applied to improve the accuracy of the selected regression models by optimizing their parameters. At the next stage, ANN and SVR were used to estimate the monthly missing values separately. Finally, the results of the previous stages were compared using the root mean square error (RMSE), and the optimal models were applied to determine the missing values of monthly temperature and precipitation of Mashhad. The results showed that the Genetic Algorithm and Ant Colony increase the accuracy of the estimation of missing rainfall data significantly more than the previous methods. The lowest error criterion (RMSE) between regression patterns is 9.8 millimeters. By genetic algorithm, this criterion is reduced to 2.56 mm, and by ant colony algorithm to 2.559. Comparison of the above methods in restoration temperature and precipitation shows that evolutionary methods (GA and ACO) are the best for estimating the missing monthly precipitation and machine learning methods (ANN and SVR) are the best to imputation missing data of monthly temperature.

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus, the idea of Debruyne’s “outlier map” is employed in this paper to identify the outlying points in the SVM classification problem. However, due to the computational reasons such as convenience and rapidity, a robust Mahalanobis distance based on the minimum covariance determinant estimator is utilized. This method has a good compatibility by the data with low dimensional structure. In addition to the classification accuracy, the margin width is used as the criterion for the performance assessment. The larger margin is more desired, due to the higher generalization ability. It should be noted that, by omission of the detected outliers using the suggested outlier map the generalization ability and accuracy of SVM are increased. This leads to the conclusion that the proposed method is very efficient in identifying the outliers. The capability of recognizing the outlying and misclassified observations for this new version of outlier map has been retained similar to the older version, which is tested on the simulated and real world data.

One of the most important problem in any statistical analysis is the existence of unexpected observations. Some observations are not a part of the study and are known as outliers. Studies have shown that the outliers affect to the performance of statistical standard methods in models and predictions. The point of this work is to provide a couple of statistical package in R software to identify outliers in circular-circular regression which is written by the author, we introduce a brief explanation about the circular data and circular regression, then the packages in R for circular regression introduced. After wand, the functions in the package CircOutlier will be described.

In many diverse scientific fields, the measurements are directions. For instance, a biologist may be measuring the direction of flight of a bird or the orientation of an animal. A series of such observations is called”directional data”. Since a direction has no magnitude, these can be conveniently represented as points on the circumference of a unit circle centered at the origin or as unit vectors connecting the origin to these points. Because of this circular representation, such observations are also called circular data. In this paper, circular data will be introduced at first and then it is explained how to calculate the mean direction, dispersion and higher moments. The solutions to many directional data problems are often not obtainable in simple closed analytical forms. Therefore, computer softwares is essential to use these methods. At the end of this paper, the CircStat’s package has been used to analyze data sets in R and Matlab softwares.

Meta-Analysis means a statistical analysis on the results of their findings is to combine a large number of independent studies. Meta-Analysis software is needed to be done for convenience, Statistical Software for Meta-Analysis is available in many statistical packages available in the present study provide software R is for Meta-Analysis. First, a brief description of the Meta-Analysis, Statistical methods used in the software and can be used to do it, then the software packages R introduced rmeta functions in the package are described for each of the an example will be given functions.

Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the location of the sample points. In this paper‎, we initially introduce the k-nearest neighbor kernel density estimator in the random left-truncation model, and then prove some of its asymptotic behaviors, such as strong uniform consistency and asymptotic normality. ‎In particular‎, ‎we show that the proposed estimator has truncation-free variance‎. ‎Simulations are presented to illustrate the results and show how the estimator behaves for finite samples‎. Moreover, the proposed estimator is used to estimate the density function of a real data set.

T‎his paper is a brief introduction to the concepts، methods ‎and ‎algorithms ‎for ‎data ‎mining ‎in ‎statistical ‎software R ‎using a‎ ‎package ‎named ‎Rattle. Rattle ‎provides a‎ ‎good ‎graphical ‎environment ‎to ‎perform ‎some ‎of ‎the ‎procedures ‎and ‎algorithms ‎without ‎the ‎need ‎for ‎programming. ‎Some ‎parts ‎of ‎the ‎package ‎will ‎be ‎explained ‎by a‎ ‎number ‎of ‎examples. ‎

In this study the effect of different levels of xanthan and pectin on water activity and sensory properties of fruit pastille formulation based on Cantaloupe puree has been studied. The effects of xanthan (0, 0.1, 0.2, 0.3 %) and pectin (0.2, 0.3, 0.4, 0.5 %) were analyzed in a completely randomized design with factorial experiment. Results of ANOVA revealed that different levels of xanthan, pectin and interaction effects between pectin and xanthan had significant influence on water activity of cantaloupe pastilles (p≤0.05). The effects of pectin and xanthan were evaluated on sensory attributes by using multivariate variance analysis (MANOVA). Generally, only pectin level significantly changed (Hotelling Trace = p≤0.05) the sensory attributes. Pectin showed significant influence (p≤0.05) on color intensity, flavor color and appearance acceptance of pastilles. Xanthan had significant effect (p≤0.05) on color intensity and surface adhesivness. Pectin-xanthan interactions also showed significant influence (p≤0.05) on color intensity and stiffness of pastilles. Results of PCA determined that texture and flavor properties were more important than appearance attributes on total acceptance of cantaloupe pastille.

In different researches, we deal with problems that need to predict the behavior of a response variable by a set of explanatory variables. Multiple regression is one of the statistical methods that is widely used in these type of situations. When there is a linear relation between explanatory variables we will faced with multi-collinearity problem that causes ordinary least squares estimators of regression coefficients not to be robust. Partial least squares regression is a multivariate method that used when there is high collinearity between explanatory variables. In this paper, this method is introduced.

Because of importance and popularity of the Normal distribution, the samples based on this distribution has been considered and the outliers are identified using cut-off values which are dependent on the sample size. A decision problem has been structured to obtain the optimal cut-off value. The problem is solved by a simulation study with a minimax rule.

The assessment of ripeness is a major part of quality evaluation and depends on several factors such as soluble solid content (SSC), acidity and firmness. Most of the methods THAT measure these qualities are destructive. So it is essential to develop an efficient and nondestructive method for measuring internal attributes of fruit. The objective of this study was to investigate visible and near infrared (Vis/NIR) spectroscopy method for prediction of SSC and pH of kiwi fruit. Near infrared spectroscopy is a fast and a nondestructive analytical technique. One of the main advantages of NIR spectroscopy is that IT allows several constituents to be measured at the same time. Transmittance determinations in the 400-1000 nm range were carried out on samples which WERE separated randomly into two groups: first group for making calibration models and second one for quality predictions. Different data preprocessing and spectra treatments such as standard normal variate transformation (SNV), multiplicative scatter correction (MSC), median filter and derivative were used to eliminate noise. Then calibration models were developed by using principal component analysis (PCA) and partial least squares (PLS). Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. The correlation coefficient and root mean square errors of prediction to soluble solids content and pH were 0.93, 0.952 and 0.259˚Brix, 0.236, respectively. The results indicated the feasibility of Vis/NIR transmittance spectral analysis to predict SSC and pH of kiwi fruit in a nondestructive way.