Adjacency matrix comparison for stochastic block models

2019 ◽  
Vol 08 (03) ◽  
pp. 1950010
Author(s):  
Guangren Yang ◽  
Songshan Yang ◽  
Wang Zhou
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Adjacency Matrix ◽  
Real Data ◽  
Finite Sample ◽  
Adjacency Matrices ◽  
Block Models

In this paper, we study whether two networks arising from two stochastic block models have the same connection structures by comparing their adjacency matrices. We conduct Monte Carlo simulations study to examine the finite sample performance of the proposed method. A real data example is used to illustrate the proposed methodology.

On the computational estimation of high order GARCH model

2021 ◽  
Vol 8 (4) ◽  
pp. 797-806
Author(s):  
A. Settar ◽  
◽  
N. I. Fatmi ◽  
M. Badaoui ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Monte Carlo Simulations ◽  
Garch Model ◽  
Real Data ◽  
Conditional Variance ◽  
High Order ◽  
Computational Estimation ◽  
Necessary And Sufficient ◽  
The Garch Model

To guarantee the non-negativity of the conditional variance of the GARCH process, it is sufficient to assume the non-negativity of its parameters. This condition was empirically violated besides rendering the GARCH model more restrictive. It was subsequently relaxed for some GARCH orders by necessary and sufficient constraints. In this paper, we generalized an approach for the QML estimation of the GARCH(p,q) parameters for all orders $p\geq 1$ and $q\geq1$ using a constrained Kalman filter. Such an approach allows a relaxed QML estimation of the GARCH without the need to identify and/or apply the relaxed constraints to the parameters. The performance of our method is demonstrated through Monte Carlo simulations and empirical applications to real data.

scholarly journals Smoothed Maximum Score Estimation of Discrete Duration Models

2019 ◽  
Vol 12 (2) ◽  
pp. 64 ◽  
Author(s):  
Sadat Reza ◽  
Paul Rilstone
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Discrete Time ◽  
Hazard Rate ◽  
Duration Models ◽  
Finite Sample ◽  
Time Duration ◽  
Maximum Score ◽  
Finite Sample Properties ◽  
Error Distributions

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples.

scholarly journals Small area estimation in the case of nonesponse

2009 ◽  
Vol 50 ◽  
Author(s):  
Vilma Nekrašaitė-Liegė
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Small Area ◽  
Finite Population ◽  
Real Data ◽  
Area Estimation ◽  
Empirical Results ◽  
Different Types ◽  
Business Survey ◽  
Repeated Samples

In this paper the effect of model and nonresponseadjustment on different types of estimators for the totals of small area domains is examined. The empirical results are based on Monte Carlo simulations with repeated samples drawn from a finite population constructed from the real data from the Lithuanian Business Survey.

scholarly journals Simulasi Monte Carlo untuk Memprediksi Persediaan Darah

2020 ◽  
Author(s):  
Rahmi Darnis ◽  
Gunadi Widi Nurcahyo ◽  
Yuhandri Yunus
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Blood Supply ◽  
Transport System ◽  
Real Data ◽  
Transportation System ◽  
Production Data ◽  
Accuracy Rate ◽  
Simulation Results

Blood is a special organ as a communication and transportation system whose job is to circulate nutrients and oxygen. As a very vital transport system and its very useful existence for many people, blood must be managed properly. Monte Carlo simulations can predict problems related to blood stock and demand for blood. The data that is processed to predict blood supply is blood production data in 2018 and 2019, Simulation results data for 2018 are compared with real data for 2019 and simulation results for 2019 are compared with real data for 2020. The results of the simulations that have been carried out have an accuracy rate of 96.21% for 2018 and 79.22% for 2019. Based on the results of the tests that have been done, it can provide information that can help in the management of forecasting future blood stocks.

A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES

2012 ◽  
Vol 29 (3) ◽  
pp. 629-641
Author(s):  
Li Cong ◽  
Jeffrey S. Racine
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Conditional Density ◽  
Binary Choice ◽  
Finite Sample ◽  
Specification Test ◽  
Conditional Density Function ◽  
Count Models ◽  
Theoretical Support ◽  
Binary Choice Models

We propose a consistent kernel-based specification test for conditional density models when the dependent variable is categorical/discrete. The method is applicable to popular parametric binary choice models such as the logit and probit specification and their multinomial and ordered counterparts, along with parametric count models, among others. The test is valid when the conditional density function contains both categorical and real-valued covariates. Theoretical support for the test and for a bootstrap-based version of the test is provided. Monte Carlo simulations are conducted to assess the finite-sample performance of the proposed method.

scholarly journals Estimation Methods of Alpha Power Exponential Distribution with Applications to Engineering and Medical Data

2020 ◽  
pp. 149-166 ◽  
Author(s):  
Mazen Nassar ◽  
Ahmed Z. Afify ◽  
Mohammed Shakhatreh
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Exponential Distribution ◽  
Real Data ◽  
Medical Data ◽  
Estimation Methods ◽  
Alpha Power ◽  
Unknown Parameters ◽  
Data Set ◽  
Distribution Parameters

This paper addresses the estimation of the unknown parameters of the alphapower exponential distribution (Mahdavi and Kundu, 2017) using nine frequentist estimation methods. We discuss the nite sample properties of the parameterestimates of the alpha power exponential distribution via Monte Carlo simulations. The potentiality of the distribution is analyzed by means of two real datasets from the elds of engineering and medicine. Finally, we use the maximumlikelihood method to derive the estimates of the distribution parameters undercompeting risks data and analyze one real data set.

scholarly journals The Generalized Transmuted Poisson-G Family of Distributions: Theory, Characterizations and Applications

2018 ◽  
pp. 759-779 ◽  
Author(s):  
Haitham Yousof ◽  
Ahmed Z Afify ◽  
Morad Alizadeh ◽  
G. G. Hamedani ◽  
S. Jahanshahi ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Order Statistics ◽  
Real Data ◽  
Model Parameters ◽  
Data Sets ◽  
Continuous Distributions ◽  
New Class ◽  
Mathematical Properties ◽  
Family Of Distributions

In this work, we introduce a new class of continuous distributions called the generalized poissonfamily which extends the quadratic rank transmutation map. We provide some special models for thenew family. Some of its mathematical properties including Rényi and q-entropies, order statistics andcharacterizations are derived. The estimations of the model parameters is performed by maximumlikelihood method. The Monte Carlo simulations is used for assessing the performance of the maximumlikelihood estimators. The ‡exibility of the proposed family is illustrated by means of two applicationsto real data sets.

scholarly journals STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS

2017 ◽  
Vol 34 (5) ◽  
pp. 985-1017 ◽  
Author(s):  
Tianxiao Pang ◽  
Terence Tai-Leung Chong ◽  
Danna Zhang ◽  
Yanling Liang
Keyword(s):  
Monte Carlo ◽  
Structural Change ◽  
Monte Carlo Simulations ◽  
Change Point ◽  
Indicator Function ◽  
The Other ◽  
Finite Sample ◽  
Mild Conditions ◽  
Finite Sample Properties ◽  
Image Position

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.

BIAS CORRECTION AT END POINTS IN KERNEL DENSITY ESTIMATION

2021 ◽  
Vol 10 (12) ◽  
pp. 3515-3531
Author(s):  
H. Bouredji ◽  
A. Sayah
Keyword(s):  
Monte Carlo ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Bias Correction ◽  
Kernel Density ◽  
Real Data ◽  
Boundary Region ◽  
Finite Sample ◽  
New Approach ◽  
The Right

In this paper, we propose a new approach of boundary correction for kernel density estimation with the support $[0,1]$, in particular at the right endpoints and we derive the theoretical properties of this new estimator and show that it asymptotically reduce the order of bias at the boundary region, whereas the order of variance remains unchanged. Our Monte Carlo simulations demonstrate the good finite sample performance of our proposed estimator. Two examples with real data are provided.

scholarly journals The normal-tangent-G class of probabilistic distributions: properties and real data modellin

2020 ◽  
pp. 827-838
Author(s):  
Fábio Silveira ◽  
Frank Gomes-Silva ◽  
Cícero Brito ◽  
Moacyr Cunha-Filho ◽  
Jader Jale ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Probability Density Function ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Series Representation ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Special Cases ◽  
Probability Density Function Pdf

This paper introduces a novel class of probability distributions called normal-tangent-G, whose submodels are parsi- monious and bring no additional parameters besides the baseline’s. We demonstrate that these submodels are iden- tifiable as long as the baseline is. We present some properties of the class, including the series representation of its probability density function (pdf) and two special cases. Monte Carlo simulations are carried out to study the behav- ior of the maximum likelihood estimates (MLEs) of the parameters for a particular submodel. We also perform an application of it to a real dataset to exemplify the modelling benefits of the class.

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