Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision
Online activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/10/6/122 |
| _version_ | 1797482623424004096 |
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| author | Sapto Wahyu Indratno Kurnia Novita Sari Mokhammad Ridwan Yudhanegara |
| author_facet | Sapto Wahyu Indratno Kurnia Novita Sari Mokhammad Ridwan Yudhanegara |
| author_sort | Sapto Wahyu Indratno |
| collection | DOAJ |
| description | Online activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method of statistical inference for online decisions. This method is built by the logarithm distribution of the likelihood function, which allows us to determine statistics using the normal statistical test approach iteratively. It means that the inference can be made in an online way every time new data arrive. Compared to classical methods (commonly, chi-squared), the advantage of this method is that it allows us to make decisions without storing large data. In particular, the novelty of this research is expressed in the algorithm, theorem, and corollary for the statistical inference procedure. In detail, this paper’s simulation discusses online statistical tests for multinomial cases and applies them to transportation data for item delivery, namely traffic density. Changes in traffic density resulted in changes to the strategy of item delivery. The goal is to obtain a minimum delivery time for the risk of losses. |
| first_indexed | 2024-03-09T22:35:03Z |
| format | Article |
| id | doaj.art-af16922940c94045ae04bfa16b2a15f4 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-09T22:35:03Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-af16922940c94045ae04bfa16b2a15f42023-11-23T18:50:08ZengMDPI AGRisks2227-90912022-06-0110612210.3390/risks10060122Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online DecisionSapto Wahyu Indratno0Kurnia Novita Sari1Mokhammad Ridwan Yudhanegara2University Center of Excellence on Artificial Intelligence for Vision, Institut Teknologi Bandung, Natural Language Processing & Big Data Analytics (U-CoE AI-VLB), Bandung 40132, West Java, IndonesiaStatistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, West Java, IndonesiaMathematics Education Department, Faculty of Teacher Training and Education, Universitas Singaperbangsa Karawang, Karawang 41361, West Java, IndonesiaOnline activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method of statistical inference for online decisions. This method is built by the logarithm distribution of the likelihood function, which allows us to determine statistics using the normal statistical test approach iteratively. It means that the inference can be made in an online way every time new data arrive. Compared to classical methods (commonly, chi-squared), the advantage of this method is that it allows us to make decisions without storing large data. In particular, the novelty of this research is expressed in the algorithm, theorem, and corollary for the statistical inference procedure. In detail, this paper’s simulation discusses online statistical tests for multinomial cases and applies them to transportation data for item delivery, namely traffic density. Changes in traffic density resulted in changes to the strategy of item delivery. The goal is to obtain a minimum delivery time for the risk of losses.https://www.mdpi.com/2227-9091/10/6/122statistics testoptimization in risk managementonline decisiondynamic network |
| spellingShingle | Sapto Wahyu Indratno Kurnia Novita Sari Mokhammad Ridwan Yudhanegara Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision Risks statistics test optimization in risk management online decision dynamic network |
| title | Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision |
| title_full | Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision |
| title_fullStr | Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision |
| title_full_unstemmed | Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision |
| title_short | Optimization in Item Delivery as Risk Management: Multinomial Case Using the New Method of Statistical Inference for Online Decision |
| title_sort | optimization in item delivery as risk management multinomial case using the new method of statistical inference for online decision |
| topic | statistics test optimization in risk management online decision dynamic network |
| url | https://www.mdpi.com/2227-9091/10/6/122 |
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