RNGforGPD: An R Package for Generation of Univariate and Multivariate Generalized Poisson Data

Li, Hesen; Demirtaş, Hakan; Chen, Ruizhe

doi:10.32614/rj-2021-007

Cited by 4 publications

(7 citation statements)

References 1 publication

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“…This count data follows a Poisson distribution [7,38]. Thus, we generated the random numbers from the multivariate generalized Poisson distribution [39,40]. We used the R data language and R package for generating simulation data and data analysis [32,40].…”

Section: Simulation Data Analysismentioning

confidence: 99%

“…Thus, we generated the random numbers from the multivariate generalized Poisson distribution [39,40]. We used the R data language and R package for generating simulation data and data analysis [32,40]. For our simulation data for keyword data analysis, we considered the mixture of Poisson distributions with two parameters, rate and dispersion [39][40][41].…”

Section: Simulation Data Analysismentioning

confidence: 99%

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Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Jun

2024

Electronics

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For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis.

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Section: Simulation Data Analysismentioning

confidence: 99%

Section: Simulation Data Analysismentioning

confidence: 99%

Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Jun

2024

Electronics

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“…We used the first variable as response variable y and others as explanatory variables x 1 and x 2 . In this paper, we used the R data language and its packages for the simulation data generation [46,47]. Table 2 shows summary statistics of all variables.…”

Section: Simulation Data Analysismentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Park

Jun

2023

Applied Sciences

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A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper.

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“…We obtained our simulation data by generating random numbers from multivariate Poisson distribution. The process for the generation of multivariate generalized Poisson data consists of two steps as follows [14,[29][30][31][32][33]:…”

Section: Simulation Study With Zero-inflated Datamentioning

confidence: 99%

“…In our simulation study, we generated four variables (X 1 , X 2 , X 3 , X 4 ) by Steps 1 and 2. In this simulation study, we used the R data language and a package provided by R for simulation data generation [14,31]. We performed two simulations with different means and correlation structures.…”

Section: Simulation Study With Zero-inflated Datamentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples

Uhm

Jun

2022

Future Internet

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Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data.

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RNGforGPD: An R Package for Generation of Univariate and Multivariate Generalized Poisson Data

Cited by 4 publications

References 1 publication

Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples

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