The Parametric and Nonparametric Estimations of Normal Distribution for Mean Parameter | Chapter 12 | Research and Applications Towards Mathematics and Computer Science Vol. 7

 This stage's objective describes the point and pause estimations on the mean that is a part of life after death parameter of sane distribution. The mean represents the allocation's central locale or average, while the standard deviation measures the spread or instability of the data.   Point estimation includes providing a specific advantage to estimate a population limit. The interval estimation supplies a range or interval of principles to estimate a population parameter, named a confidence pause. These estimations are estimated for one parametric method by using the maximum prospect, Bayesian, and Markov Chain Monte Carlo procedures. The nonparametric method consists of the start operating system and the Jackknife arrangements. Maximum likelihood is the familiar method to approximate parameters cause there are the characteristics of the unbiased estimator, consistency, and adeptness estimator. The Bayesian method is a mathematical approach based on feasibility, prior, and posterior distribution. The Markov Chain Gambling establishment technique influences Markov chains and random sampling to estimate complex anticipation distributions and involves the Bayesian method. The start operating system and Jackknife forms are the resampling techniques by repeatedly illustration samples from the available dossier.

Author(s) Details:

Autcha Araveeporn,
Department of Statistics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok-10520, Thailand.

Somsri Banditvilai,
Department of Statistics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok-10520, Thailand.

Please see the link here: https://stm.bookpi.org/RATMCS-V7/article/view/12883

If You Can’t Measure it You Can’t Manage it - Quantitative Analysis of Cyber Risk Prediction and Mitigation | Chapter 7 | Current Topics on Business, Economics and Finance Vol. 5

 Cyber rupture incidents have increased severely during COVID-19 pandemic and maintain a cyclical trend skilled after. Data breach occurrence result in harsh financial loss and reputational damage to trade, government, healthcare and educational organizations. Compared to sufficient amount of cyber risk case in economic and IT system rule, seldom investigations of high-tech risk have been made in all-inclusive perspective, In order to fill this gap, we suggest a Bayesian generalized linear assorted model to analyze data rift incidents chronicle since 2001. Our model captures the dependency 'tween frequency and severity of high-tech losses, and the behavior of high-tech attacks on entities across time. Risk traits such as types of breach, types of institution, entity locations in chronicle, as well as time current effects are taken into concern when investigating breach repetitions. A statistical predicting model is generated under actuarial mathematics frame, accompanying flexible input usable such as location and arranging types. Predictions and implications of the proposed model in adventure risk management and cyber protection rate filing are discussed and pictorial. Our results show that both geological area and business type play significant duties in measuring cyber risks. The consequences of our predictive data provide numerical bills loss level that can be took advantage of by various kinds of arrangements and design their risk mitigation strategies.

Author(s) Details:

Meng Sun,
Simon Fraser University, BC, Canada.

Please see the link here: https://stm.bookpi.org/CTBEF-V5/article/view/10490