scholarly journals Parametric Estimation of Diffusion Processes: A Review and Comparative Study

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 859
Author(s):  
Alejandra López-Pérez ◽  
Manuel Febrero-Bande ◽  
Wencesalo González-Manteiga
Keyword(s):  
Continuous Time ◽  
Diffusion Processes ◽  
Real Data ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Finite Sample ◽  
Interest Rate Dynamics ◽  
Discrete Time Observations ◽  
The Impact

This paper provides an in-depth review about parametric estimation methods for stationary stochastic differential equations (SDEs) driven by Wiener noise with discrete time observations. The short-term interest rate dynamics are commonly described by continuous-time diffusion processes, whose parameters are subject to estimation bias, as data are highly persistent, and discretization bias, as data are discretely sampled despite the continuous-time nature of the model. To assess the role of persistence and the impact of sampling frequency on the estimation, we conducted a simulation study under different settings to compare the performance of the procedures and illustrate the finite sample behavior. To complete the survey, an application of the procedures to real data is provided.

scholarly journals System of greening of urban spaces and its role in optimization of the micro- and bioclimate environment

2019 ◽  
Vol 135 ◽  
pp. 03060
Author(s):  
Adham Giyasov
Keyword(s):  
Urban Area ◽  
Real Data ◽  
Urban Spaces ◽  
Urban Greening ◽  
Open Spaces ◽  
Natural Landscape ◽  
Framework System ◽  
The Impact ◽  
Urban Territory

The article is devoted to the issue of sustainable development of green areas of the urbanized environment. The main problems of the functional filling of the open spaces of the urban territory with a green structure in the natural framework system and the impact of landscaping on the ecological environment are considered. The role of the urban greening system in multi-storey buildings with the aim of optimizing the micro- and bioclimatic environment is studied. Examples of using landscaping techniques as an important component of the natural landscape as means to compensate for anthropogenic impact are given. Real data have been obtained on the microclimatic and bioclimatic effectiveness of landscaping of various sizes, which determine the planning structure of the urban area with the appropriate techniques and principles of landscaping in the field of dendrology of cities of southern latitude. Variants of intensive landscaping of the urban area are proposed.

On Estimating the Survival Function for the Patients Suffer from the Lung Cancer Disease

2018 ◽  
pp. 411
Author(s):  
Abbas N. Salman ◽  
Ibtehal H. Farhan ◽  
Maymona M. Ameen ◽  
Adel Abdulkadhim Hussein
Keyword(s):  
Lung Cancer ◽  
Survival Function ◽  
Real Data ◽  
Shrinkage Estimator ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Cancer Disease ◽  
Statistical Indicator

          In this paper, the survival function has been estimated for the patients with lung cancer using different parametric estimation methods depending on sample for completing real data which explain the period of survival for patients who were ill with the lung cancer based on the diagnosis of disease or the entire of patients in a hospital for a time of two years (starting with 2012 to the end of 2013). Comparisons between the mentioned estimation methods has been performed using statistical indicator mean squares error, concluding that the estimation of the survival function for the lung cancer by using pre-test singles stage shrinkage estimator method was the best   . 

Improving board diversity around the world: the role of institutional investors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Badar Alshabibi
Keyword(s):  
Institutional Investors ◽  
Estimation Methods ◽  
Board Diversity ◽  
Content Type ◽  
Negatively Associated ◽  
Ownership Structures ◽  
Institutional Settings ◽  
Age Diversity ◽  
The Impact

Purpose This study aims to examine the role of institutional investors in improving board diversity for the companies in which they invest (investee companies) using evidence from corporate board characteristics across the globe. Additionally, this study also investigates the association between institutional investors and board diversity under various institutional settings, including varying economic conditions (pre-crisis, crisis and post-crisis), legal systems and ownership structures. Design/methodology/approach Using a sample collected from 15 countries for the period 2006 to 2012, the paper uses panel data analysis to examine the association between institutional investors and board diversity. Findings The study provides evidence that institutional investors do not promote board diversity and show that in general there is no association between institutional ownership and various board diversity attributes such as gender, age, nationality and education. However, the study finds that institutional investors are positively associated with the educational diversity of boards during times of crisis and are negatively associated with board age diversity during pre-crisis and post-crisis periods. Furthermore, while in common law countries institutional investors are found to be negatively associated with board age diversity, they do not influence board diversity outcomes (i.e. gender, age, nationality and education) in civil law countries. The results also show that the associations between institutional investors and board diversity are mixed and insignificant according to different ownership structures (family and non-family owned firms). The main findings of the study are robust and apply to various estimation methods. Originality/value This study provides a unique perspective on the impact of institutional investors on board diversity using a sample collected from 15 countries. Furthermore, the study provides an insight that the institutional settings should be considered when investigating the activism of institutional investors in improving governance practices.

NONPARAMETRIC NONSTATIONARITY TESTS

2013 ◽  
Vol 30 (1) ◽  
pp. 127-149 ◽  
Author(s):  
Federico M. Bandi ◽  
Valentina Corradi
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Diffusion Processes ◽  
Nonlinear Process ◽  
Additive Functional ◽  
Finite Sample ◽  
Occupation Times ◽  
Discrete Time Case ◽  
Continuous Time Processes

We propose additive functional-based nonstationarity tests that exploit the different divergence rates of the occupation times of a (possibly nonlinear) process under the null of nonstationarity (stationarity) versus the alternative of stationarity (nonstationarity). We consider both discrete-time series and continuous-time processes. The discrete-time case covers Harris recurrent Markov chains and integrated processes. The continuous-time case focuses on Harris recurrent diffusion processes. Notwithstanding finite-sample adjustments discussed in the paper, the proposed tests are simple to implement and rely on tabulated critical values. Simulations show that their size and power properties are satisfactory. Our robustness to nonlinear dynamics provides a solution to the typical inconsistency problem between assumed linearity of a time series for the purpose of nonstationarity testing and subsequent nonlinear inference.

scholarly journals Comparison of Some Semi-parametric Methods in Partial Linear Single-Index Model

2021 ◽  
Vol 27 (130) ◽  
pp. 170-184
Author(s):  
Huda Yahya Ahmed ◽  
Munaf Yousif Hmood
Keyword(s):  
Parametric Estimation ◽  
Estimation Methods ◽  
Model Estimation ◽  
Finite Sample ◽  
Two Stage ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
The Mean ◽  
Partial Linear

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

scholarly journals Identification and Estimation in Non-Fundamental Structural VARMA Models

2019 ◽  
Vol 87 (4) ◽  
pp. 1915-1953 ◽  
Author(s):  
Christian Gouriéroux ◽  
Alain Monfort ◽  
Jean-Paul Renne
Keyword(s):  
Dynamic Properties ◽  
Moving Average ◽  
Real Data ◽  
Identification Problem ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Linear Transformations ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Matrix Polynomials

Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is “dynamic” and is a consequence of the fact that, even if a SVARMA admits a non-invertible moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.

scholarly journals The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

2021 ◽  
Author(s):  
Dariusz Brzezinski ◽  
Leandro L. Minku ◽  
Tomasz Pewinski ◽  
Jerzy Stefanowski ◽  
Artur Szumaczuk
Keyword(s):  
Data Streams ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Local Data ◽  
Real World Data ◽  
Minority Class ◽  
The Impact ◽  
Concept Drifts

AbstractClass imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers’ reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.

scholarly journals A teaching tool about the fickle p value and other statistical principles based on real-life data

2021 ◽  
Author(s):  
Salem Alawbathani ◽  
Mehreen Batool ◽  
Jan Fleckhaus ◽  
Sarkawt Hamad ◽  
Floyd Hassenrück ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Real Life ◽  
Real Data ◽  
P Value ◽  
Life Data ◽  
Real Life Data ◽  
Random Samples ◽  
Poor Understanding ◽  
The Impact

AbstractA poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.

scholarly journals A New Probability Distribution for Modeling Failure and Service Times: Properties, Copulas and Various Estimation Methods

2021 ◽  
Vol 9 (3) ◽  
pp. 555-586
Author(s):  
Hanaa Elgohari ◽  
Mohamed Ibrahim ◽  
Haitham Yousof
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Real Data ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Finite Sample ◽  
Simulation Experiments ◽  
Mean Squared Errors

In this paper, a new generalization of the Pareto type II model is introduced and studied. The new density canbe “right skewed” with heavy tail shape and its corresponding failure rate can be “J-shape”, “decreasing” and “upside down (or increasing-constant-decreasing)”. The new model may be used as an “under-dispersed” and “over-dispersed” model. Bayesian and non-Bayesian estimation methods are considered. We assessed the performance of all methods via simulation study. Bayesian and non-Bayesian estimation methods are compared in modeling real data via two applications. In modeling real data, the maximum likelihood method is the best estimation method. So, we used it in comparing competitive models. Before using the the maximum likelihood method, we performed simulation experiments to assess the finite sample behavior of it using the biases and mean squared errors.

scholarly journals A new one-parameter lifetime distribution and its regression model with applications

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246969
Author(s):  
M. S. Eliwa ◽  
Emrah Altun ◽  
Ziyad Ali Alhussain ◽  
Essam A. Ahmed ◽  
Mukhtar M. Salah ◽  
...  
Keyword(s):  
Regression Model ◽  
Real Data ◽  
Quantile Function ◽  
Lifetime Distribution ◽  
Least Square ◽  
Estimation Methods ◽  
Logistic Distribution ◽  
Data Sets ◽  
Finite Sample ◽  
Lifetime Distributions

Lifetime distributions are an important statistical tools to model the different characteristics of lifetime data sets. The statistical literature contains very sophisticated distributions to analyze these kind of data sets. However, these distributions have many parameters which cause a problem in estimation step. To open a new opportunity in modeling these kind of data sets, we propose a new extension of half-logistic distribution by using the odd Lindley-G family of distributions. The proposed distribution has only one parameter and simple mathematical forms. The statistical properties of the proposed distributions, including complete and incomplete moments, quantile function and Rényi entropy, are studied in detail. The unknown model parameter is estimated by using the different estimation methods, namely, maximum likelihood, least square, weighted least square and Cramer-von Mises. The extensive simulation study is given to compare the finite sample performance of parameter estimation methods based on the complete and progressive Type-II censored samples. Additionally, a new log-location-scale regression model is introduced based on a new distribution. The residual analysis of a new regression model is given comprehensively. To convince the readers in favour of the proposed distribution, three real data sets are analyzed and compared with competitive models. Empirical findings show that the proposed one-parameter lifetime distribution produces better results than the other extensions of half-logistic distribution.

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