توزیع گسسته متوازن وایبل و مدل گسسته مقدار خودبازگشتی متناظر آن: ویژگی ها، برآورد و آنالیز داده های شمارشی فوت ناشی از COVID-19

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه آمار، واحد قائمشهر، دانشگاه آزاد اسلامی، قائمشهر، ایران

چکیده

در این مقاله به معرفی توزیع گسسته جدید وایبل بر اساس روش توزیع گسسته متوازن که حافظ گشتاورهای

جزئی بین دو نسخه گسسته و پیوسته توزیع ها است، می پردازیم. برخی ویژگی های آماری توزیع جدید و انواع پراکندگی

توزیع مدنظر بر اساس انتخاب های گوناگونی از پارامترها ارائه می گردد. علاوه بر معرفی نسخه جدید گسسته متوازن وایبل،

مدل خودبازگشتی گسسته مقدار با نوفه هایی از توزیع گسسته مدنظر را ارائه می نماییم و به بررسی روش های مختلف

برآورد پارامترهای مدل می پردازیم. با استفاده از داده های فوت ناشی از 19-COVID در کشورهای کوبا، مالاوی و

ازبکستان، به بررسی کارایی فرایند جدید در برازش داده های واقعی در مقایسه با برخی مدل های خودبازگشتی گسسته مقدار

کلاسیک می پردازیم. در پایان پیش بینی فرایند با استفاده از دو رویکرد کلاسیک و بوت استرپ غربال، بر اساس داده های

واقعی نیز بررسی می شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Balanced Discrete Weibull Distribution and Its Corresponding Integer-value Autoregressive Model: Properties, Estimation and Analysis of Counting Death of COVID-19 Data

نویسندگان [English]

  • Seyedeh Mahbubeh Hoseini Baladezaei
  • Einolah Deiri
  • Ezzatallah Baloui Jamkhaneh
Department of Statistics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
چکیده [English]

In this paper, we introduce a new discrete Weibull distribution based on the balanced discretization method, which preserves the partial moments between the two discrete and continuous versions

of the distributions. Some statistical features of the new distribution and different kinds of dispersion of

the proposed distribution are presented based on various selections of parameters. In addition to introducing the new version of balanced discrete Weibull, we provide the integer-valued autoregressive model

with the innovation of the proposed discrete distribution and evaluate different methods for estimating the

model parameters. Using the counts of death of the COVID-19 data in Cuba, Malawi and Uzbekistan, we

appraise the performance of the new process in fitting real data to some classical integer-valued autoregressive models. Finally, the forecasting of the process is checked based on real data using both classical

and sieve bootstrap approaches

کلیدواژه‌ها [English]

  • Balanced discrete Weibull
  • Partial mean preserving
  • Integer-valued autoregressive model
  • Forecasting of process
  • COVID-19
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