Rahim Mahmoudvand

Rahim Mahmoudvand
Buali Sina University · Department of Statistics

About

47
Publications
18,356
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725
Citations
Citations since 2016
26 Research Items
547 Citations
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
Introduction
I am assistant professor in the department of statistics at Bu-Ali Sina University, Hamedan, Iran. My interest topics are Singular Spectrum Analysis, Time series analysis, application of statistics, Actuarial science, Bonus-Malus Systems. Pension funds and risk assessments.
Skills and Expertise

Publications

Publications (47)
Article
Full-text available
Physical evaluation of active soil layer can be a suitable indicator for detecting climate change trends, especially the temporal study of soil and air temperatures. Using the singular spectrum analysis (SSA), trends, oscillatory components, and the degree of the coincidence of the soil temperature (ST) versus air temperature (AT) and precipitation...
Article
Full-text available
In this paper, we develop a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology is constructed by integrating ideas from functional data analysis and univariate SSA. Specifically, we introduce a trajectory operator in the functional world, which is equivalent to the traj...
Article
A proper understanding and analysis of the processes involved in seasonal precipitation variability and dynamics is essential to provide reliable information about climate change and how it can affect matters of critical importance such as water availability and agricultural productivity in urban cities. Precipitation data, as many other time serie...
Article
Full-text available
A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference....
Article
In recent years singular spectrum analysis (SSA) has been used as a powerful technique to analyze time series, including theoretical developments and application to many practical problems. However, no inclusive theoretical approach has been discussed regarding the construction of confidence intervals for forecasts. Due to the prominent role of pre...
Article
The window length, L, is the first parameter that must be specified in Singular Spectrum Analysis (SSA) for time series analysis. A large window length has a potential to produce a good model fit, but it is unlikely to produce a parsimonious forecasting model. In this paper, we propose a new parsimonious vector forecasting model which uses an optim...
Preprint
Full-text available
In this paper, we introduce a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology is developed by integrating ideas from functional data analysis and univariate SSA. We explore the advantages of the functional SSA in terms of simulation results and with an application to...
Article
The goal of this study is to introduce an Asymmetric Uniform-Laplace (AUL) distribution. We present a detailed theoretical description of this distribution. We try to estimate the parameters of AUL distribution using the maximum likelihood method. Since the likelihood approach results in complicated forms, we suggest a bootstrap-based approach for...
Chapter
Banks and financial institutions are exposed with credit risk, liquidity risk, market risk, operational risk, and others. Credit risk often comes from undue concentration of loan portfolios. Among the diversity of tools available in literature for risk measurement, in our study the Coefficient of Variation (CV) was chosen taking into account that i...
Book
Full-text available
This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical aspects underlying SSA, but also provides a comprehensive gu...
Chapter
This chapter presents three main applications of using Singular Spectrum Analysis (SSA): change point detection, gap filling/missing value imputation, and filtering/denoising. A concise description of the main idea along with technical background with various practical illustrations with associated R codes are given in this chapter. Both univariate...
Chapter
When multiple time series are observed, we are usually interested in the internal structure of each, and at the same time their joint structure, or the dependency among series. Accordingly, the second chapter of this book is dedicated to this vital concept. In this chapter, the basic univariate Singular Spectrum Analysis (SSA) is extended in a fair...
Chapter
The core intention of this chapter is providing the reader with more theoretical information regarding technical background of the Singular Spectrum Analysis (SSA) approach so that each part of the presented technique can be easily extended/modified according to the users’ desirer/applications. The possibility of using two window lengths for recons...
Chapter
A concise description of univariate Singular Spectrum Analysis (SSA) is presented in this chapter. A step-by-step guide for performing filtering, forecasting as well as forecasting interval using univariate SSA and associated R codes is also provided. After reading this chapter, the reader will be able to select two basic, but very important, choic...
Article
Singular spectrum analysis (SSA) is a nonparametric method for time series analysis and forecasting that incorporates elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems, and signal processing. Although this technique has shown to be advantageous over traditional model‐based methods, in part...
Preprint
Full-text available
Banks and financial institutions are exposed with credit risk, liquidity risk, market risk, operational risk and others. Credit risk often comes from undue concentration of loan portfolios. Among the diversity of tools available in literature for risk measurement, in our study the Coefficient of Variation (CV) was chosen taking into account that it...
Article
Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarit...
Book
This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical aspects underlying SSA, but also provides a comprehensive gu...
Article
Singular Spectrum Analysis (SSA) is a relatively simple and powerful method in the area of time series analysis that is mainly based on matrix analysis. In this paper, we present a methodological comparison between the univariate and multivariate versions of SSA. Additionally, we explore the advantages of multivariate SSA in terms of theoretical re...
Article
Full-text available
There is a vast literature on Bonus-Malus System (BMS), in which a policyholders responsible for positive claims will be penalised by a malus and the policyholders who had no claim will be rewarded by a bonus. In this paper, we present an optimal BMS using finite mixture models. We conduct a numerical study to compare the new model with the current...
Article
In this paper, we investigate the possibility of using multivariate singular spectrum analysis (SSA), a nonparametric technique in the field of time series analysis, for mortality forecasting. We consider a real data application with 9 European countries: Belgium, Denmark, Finland, France, Italy, Netherlands, Norway, Sweden, and Switzerland, over a...
Article
Singular spectrum analysis (SSA) is a powerful nonparametric method in the area of time series analysis that has shown its capability in different applications areas. SSA depends on two main choices: the window length L and the number of eigentriples used for grouping r. One of the most important issues when analyzing time series is the forecast of...
Article
In this paper, we propose an integrated approach to adjust the premium relativities in a bonus-malus system by using the information of the first claim time (expressed in terms of sub-period in a year) and the number of claims reported by individual policyholder. We provide a formal representation for the newly proposed structure and derive the ana...
Article
Correlation analysis is one of the standard and most informative descriptive statistical tools when studying relationships between variables in bivariate and multivariate data. However, when data is contaminated with outlying observations, the standard Pearson correlation might be misleading and result in erroneous outcomes. In this paper, we propo...
Article
Ambient electromagnetic interferences at the site of investigation often degrade the signal quality of the Surface-NMR measurements leading to inaccurate estimation of the signal parameters. This paper proposes a new powerful de-noising method based on singular spectrum analysis (SSA), which is a nonparametric method for analyzing time series. SSA...
Article
This paper introduces a new algorithm for gap filling in univariate time series by using SSA. In this algorithm, the data before the missing values and the data after the missing values (in reverse order) are treated as two separate time series. Then using the recurrent SSA forecasting algorithm, two estimations of the missing values are obtained,...
Article
Full-text available
Singular spectrum analysis (SSA) is a relatively new and powerful non-parametric time series analysis technique that has demonstrated its capability in forecasting different time series in various disciplines. In this paper, we study the feasibility of using the SSA to perform mortality forecasts. Comparisons are made with the Hyndman–Ullah model,...
Article
The purpose of this paper is to categorize and analyze various risk factors in Irans gas refineries for insurance purposes. Using the failure modes and effects analysis method as a subset of probability risk assessment technique and gas refineries data for the period March 2011 till March 2012, risk priorities numbers are calculated from the perspe...
Article
Several modifications of the Laplace distribution have been introduced and applied in various fields up to this day. In this paper, we introduce a modified symmetric version of the classical Laplace distribution. We provide a comprehensive theoretical description of this distribution. In particular, we derive the formulas for the kth moment, quanti...
Article
Full-text available
The aim of this paper is to study the effect of outliers on different parts of singular spectrum analysis (SSA) from both theoretical and practical points of view. The rank of the trajectory matrix, the magnitude of eigenvalues, reconstruction, and forecasting results are evaluated using simulated and real data sets. The performance of both recurre...
Chapter
A Bonus-Malus system (BMS) is one of the types of experience ratemaking methods in automobile insurance in which the future premiums are adjusted according to the insured’s claim history. Usually it is assumed the considered portfolio for designing a BMS is closed, where the policyholders only have movement between specified classes in the portfoli...
Article
Organizations use many tools to perform risk analysis. Failure Mode and Effects Analysis (FMEA) is one such tool which ranks highly as a modern and widely used tool. Despite its advantages, FMEA experiences limitations within the insurance industry. The main aim of this paper is to enhance the capability of using FMEA in the insurance industry. As...
Article
The Lee-Carter model and its extensions are the most popular methods in the �eld of forecasting mortality rate. But, in spite of introducing several di�erent methods in forecasting mortality rate so far, there is no general method applicable to all situations. Singular Spectrum Analysis (SSA) is a relatively new, powerful and nonparametric time ser...
Article
Full-text available
In this paper we consider a formula to get the exact number of nonnegative integer solution of the equality a1x1 + a2x2 + … + arxr = n where a1, a2, … , ar and n are fixed integers. Using the obtained formula, we provide a program to list the solutions for every n and a1, … , ar by Pascal compiler. We then obtain the distribution of an arbitrary li...
Data
Frangos and Vrontos (2001) proposed an optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. In this paper, we introduce a generalized form of those obtained pre-viously. KEYWORDS Bonus-malus systems, frequency component, severity component.
Article
There are two main parameters in Singular Spectrum Analysis (SSA). The aim of this study is to determine whether the optimal values of these parameters are different for reconstruction and forecasting stages, and if those are worth the extra computational effort and time which they require. Here, we evaluate these issues using simulation study.
Article
Full-text available
The different forms of the multivariate singular spectrum analysis (SSA) and their associated forecasting algorithms are considered from both theoretical and practical points of view. The new multivariate vector forecasting algorithm is introduced and its uniqueness is evaluated. The performance of the new multivariate forecasting algorithm is asse...
Data
Full-text available
We study the distribution of discounted collective risk model where the counting process is Poisson. For the model considered here, we obtain mean, variance and moment generating function (m.g.f) of the model. To do this, we use two approaches. In the first approach we use classical methods to obtain the mean and variance. In the second approach we...
Article
Full-text available
The optimal value of the window length in singular spectrum analysis (SSA) is considered with respect to the concept of separability between signal and noise component, from the theoretical and practical perspective. The theoretical results confirm that for a wide class of time series of length N, the suitable value of this parameter is median {1,...
Article
To find the optimal value of window length in singular spectrum analysis (SSA), we consider the concept of separability between the signal and noise component. The theoretical results confirm that for a wide class of time series, the suitable value of this parameter is median{1,…,T} with the series of length T. The theoretical results obtained here...
Article
Full-text available
Hankel matrices are an important family of matrices that play a fundamental role in diverse fields of study, such as computer science, engineering, mathematics and statistics. In this paper, we study the behavior of the singular values of the Hankel matrix by changing its dimension. In addition, as an application, we use the obtained results for ch...
Article
Full-text available
In this paper we examine the effect of outlier/leverage point on the accuracy measures in the linear regression models. We use the coefficient of determination, which is a measure of model adequacy, to compare the effect of filtering approach on the least squares estimates. We also compare the performance of the filter-based approach with several r...
Article
Full-text available
We present a simple and fast method for counting the number of nonnegative integer solutions to the equality a 1 x 1 +a 2 x 2 +⋯+a r x r =n, where a 1 ,a 2 ,⋯,a r and n are positive integers. As an application, we use the method for finding the number of solutions of a Diophantine inequality.
Article
Full-text available
In this article we introduce an approximately unbiased estimator for the population coefficient of variation, τ, in a normal distribution. The accuracy of this estimator is examined by several criteria. Using this estimator and its variance, two approximate confidence intervals for τ are introduced. The performance of the new confidence intervals i...
Article
Full-text available
In this paper, we obtain bounds for the population coefficient of variation (CV) in Bernoulli, Discrete Uniform, Normal and Exponential distributions. We also show that the sample coefficient of variation (cv) is not an accurate estimator of the population CV in the above indicated distributions. Finally we provide some suggestions based on the Max...
Article
Full-text available
In this paper, we introduce a new approximation for the null distribution of the likelihood ratio test for the general case. We compare the the critical values obtained by the new approximation to the values which are obtained by the exact distribution for the cases k=1, 2 to test the accuracy of the new approximation. Also, we compare the results...

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