Simulation Study to Compare the Random Data Generation from Bernoulli Distribution in Popular Statistical Packages

2010 ◽  
Author(s):  
Nadeem Shafique Butt ◽  
Manash Pratim Kashyap ◽  
Dibyojyoti Bhattacharjee
Keyword(s):  
Simulation Study ◽  
Data Generation ◽  
Bernoulli Distribution ◽  
Random Data

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

scholarly journals Offline Hand Written Urdu Word Spotting Using Random Data Generation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 131119-131136
Author(s):  
Faiq Faizan Farooqui ◽  
Muhammad Hassan ◽  
Muhammad Shahzad Younis ◽  
Muhammad Kashif Siddhu
Keyword(s):  
Data Generation ◽  
Random Data ◽  
Word Spotting

AUTOMATIC TEST DATA GENERATION FOR PROGRAM PATHS USING GENETIC ALGORITHMS

2002 ◽  
Vol 12 (06) ◽  
pp. 691-709 ◽  
Author(s):  
PAULO MARCOS SIQUEIRA BUENO ◽  
MARIO JINO
Keyword(s):  
Test Data ◽  
Input Data ◽  
Initial Population ◽  
Test Data Generation ◽  
Data Generation ◽  
Random Data ◽  
Dynamic Technique ◽  
Path Testing ◽  
Automatic Test Data Generation ◽  
A New Technique

A new technique and tool are presented for test data generation for path testing. They are based on the dynamic technique and on a Genetic Algorithm, which evolves a population of input data towards reaching and solving the predicates along the program paths. We improve the performance of test data generation by using past input data to compose the initial population for the search. An experiment was done to assess the performance of the techniques compared to that of random data generation.

scholarly journals A Causal Perspective on OSIM2 Data Generation, with Implications for Simulation Study Design and Interpretation

2015 ◽  
Vol 3 (2) ◽  
pp. 177-187 ◽  
Author(s):  
Susan Gruber
Keyword(s):  
Simulation Study ◽  
Real World ◽  
Structural Equation ◽  
Medical Condition ◽  
Causal Effect ◽  
Causal Structure ◽  
Data Generation ◽  
Real World Data ◽  
World Data ◽  
Simulation Scheme

AbstractResearch by the Observational Medical Outcomes Partnership (OMOP) has focused on developing and evaluating strategies to exploit observational electronic data to improve post-market prescription drug surveillance. A data simulator known as OSIM2 developed by the OMOP statistical methods group has been used as a testbed for evaluating and comparing different estimation procedures for detecting adverse drug-related events from data similar to that found in electronic insurance claims data. The simulation scheme produces a longitudinal dataset with millions of observations designed to closely match marginal distributions of important covariates in a known dataset. In this paper we provide a non-parametric structural equation model for the data generating process and construct the associated directed acyclic graph (DAG) depicting the causal structure. These representations reveal key differences between simulated and real-world data, including a departure from longitudinal causal relationships, absence of (presumed) sources of bias and time ordering of covariates that conflicts with reality. The DAG also reveals the presence of unmeasured baseline confounding of the causal effect of a drug on a subsequent medical condition. Conclusions naively drawn from this simulation study could mislead an investigator trying to gain insight into estimator performance on real data. Applying causal inference tools allows us to draw more informed conclusions and suggests modifications to the simulation scheme that would more closely align simulated and real-world data.

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