Distinguishing between model- and data-driven inferences for high reliability statistical predictions

Hund, Lauren; Schroeder, Benjamin; Rumsey, Kellin N.; Huerta, Gabriel

doi:10.1016/j.ress.2018.07.017

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…Statistical speaking, the ð f QFÞ is employed to generate and extend quantile analogs of conventional momentbased descriptive metrics. This function f QF may be used to generate random data that corresponds to the density specified in (1). Analytical properties of NLD can be validated by utilizing the simulated data from (24).…”

Section: Quantile Functionmentioning

confidence: 99%

“…In anticipating the long-term reliability of electronic components and devices, identifying failure distribution and assessing the failure mechanism is always critical in reliability analysis. To characterize the behavior of the products, many lifespan models have been proposed [1]. These lifetime models have been categorized according to increasing, decreasing, and constant failure patterns [2,3].…”

Section: Introductionmentioning

confidence: 99%

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The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications

Salem

Khan

Ayed

et al. 2021

Journal of Function Spaces

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The lognormal distribution is more extensively used in the domain of reliability analysis for modeling the life-failure patterns of numerous devices. In this paper, a generic form of the lognormal distribution is presented that can be applied to model many engineering problems involving indeterminacies in reliability studies. The suggested distribution is especially effective for modeling data that are roughly symmetric or skewed to the right. In this paper, the key mathematical properties of the proposed neutrosophic lognormal distribution (NLD) have been derived. Throughout the study, detailed examples from life-test data are used to confirm the mathematical development of the proposed neutrosophic model. The core ideas of the reliability terms, including the neutrosophic mean time failure, neutrosophic hazard rate, neutrosophic cumulative failure rate, and neutrosophic reliability function, are addressed with examples. In addition, the estimation of two typical parameters of the NLD by mean of maximum likelihood (ML) approach under the neutrosophic environment is described. A simulation experiment is run to determine the performance of the estimated parameters. Simulated findings suggest that ML estimators effectively estimate the unknown parameters with a large sample size. Finally, a real dataset on ball bearings failure times has been considered an application of the proposed model.

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Section: Quantile Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications

Salem

Khan

Ayed

et al. 2021

Journal of Function Spaces

View full text Add to dashboard Cite

show abstract

“…Researchers have used various intelligent models to predict daily evaporation, solar radiation, water retention, soil-saturation permeability, and crop yield [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Dehghanisanij

Emami

Achite

et al. 2021

Water

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Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.

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“…Researchers are using data driven models to get an accurate prediction by utilizing the available data. With data driven model, the ML algorithms play an important role in attaining better accuracy [12,13]. Though, ML has gained much attention in many areas, the ML techniques still have some restrictions when it is used in a purely data-driven manner.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

Talasila¹,

Prasad²,

Reddy³

et al. 2020

IJIES

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Agriculture planning plays a significant role in economic growth and the food security of agro-based country. Crop yield prediction and selection of crops are the most challenging tasks in agricultural domain and it depends on different parameters such as production rate, market price and government policies. Among the two primary tasks, the crop yield prediction is one of the most demanding tasks for every nation. Due to uncertain climatic changes, farmers are struggling to attain a satisfactory amount of yield from the crops. Many researchers have studied on the prediction of weather, prediction of yield rate of crop, crop classification and soil classification for agriculture planning using statistical methods or machine learning techniques. This study focuses on the prediction of major crops in Andhra Pradesh region and presents an enhanced algorithm known as Deep Convolutional Regression Network (DCRN), which is trained and tested on agricultural data collected from farmers. The experimental results showed that the DCRN method achieved nearly 97% prediction accuracy when compared with existing methods like Decision Tree (DT), Self-Organizing Map (SOM).

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Distinguishing between model- and data-driven inferences for high reliability statistical predictions

Cited by 10 publications

References 40 publications

The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications

The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

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