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Relevant bibliographies by topics / Counting process

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Journal articles

Academic literature on the topic 'Counting process'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Counting process"

1

Watson, Ray, and Paul Yip. "A bivariate counting process." Journal of Applied Probability 30, no. 2 (1993): 353–64. http://dx.doi.org/10.2307/3214844.

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We consider a bivariate Markov counting process with transition probabilities having a particular structure, which includes a number of useful population processes. Using a suitable random time-scale transformation, we derive some probability statements about the process and some asymptotic results. These asymptotic results are also derived using martingale methods. Further, it is shown that these methods and results can be used for inference on the rate parameters for the process. The general epidemic model and the square law conflict model are used as illustrative examples.

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2

Camos, Valérie. "Coordination process in counting." International Journal of Psychology 38, no. 1 (2003): 24–36. http://dx.doi.org/10.1080/00207590244000269.

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3

Watson, Ray, and Paul Yip. "A bivariate counting process." Journal of Applied Probability 30, no. 02 (1993): 353–64. http://dx.doi.org/10.1017/s002190020011736x.

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Abstract:

We consider a bivariate Markov counting process with transition probabilities having a particular structure, which includes a number of useful population processes. Using a suitable random time-scale transformation, we derive some probability statements about the process and some asymptotic results. These asymptotic results are also derived using martingale methods. Further, it is shown that these methods and results can be used for inference on the rate parameters for the process. The general epidemic model and the square law conflict model are used as illustrative examples.

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4

Lee, Lena. "Automating the Cell Counting Process." Genetic Engineering & Biotechnology News 34, no. 15 (2014): 34–35. http://dx.doi.org/10.1089/gen.34.15.15.

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5

Freud, Thomas, and Pablo M. Rodriguez. "The Bell–Touchard counting process." Applied Mathematics and Computation 444 (May 2023): 127741. http://dx.doi.org/10.1016/j.amc.2022.127741.

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6

Tsaniya, Ulya, Triastuti Wuryandari, and Dwi Ispriyanti. "ANALISIS SURVIVAL PADA DATA KEJADIAN BERULANG MENGGUNAKAN PENDEKATAN COUNTING PROCESS." Jurnal Gaussian 11, no. 3 (2022): 377–85. http://dx.doi.org/10.14710/j.gauss.11.3.377-385.

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Asthma is a disorder that attacks the respiratory tract and causes bronchial hyperactivity to various stimuli characterized by recurrent episodic symptoms such as wheezing, coughing, shortness of breath, and heaviness in the chest. Asthma sufferers will experience exacerbations, namely episodes of asthma recurrence which gradually worsens progressively accompanied by the same symptoms. The length of time a person experiences an exacerbation can be influenced by various factors. To analyze this, the Cox regression model can be used which is within the scope of survival analysis where time is the dependent variable. In the survival analysis, asthma exacerbations were identical/recurrent events where the individual experienced the event more than once during the study. If the survival data contains identical/recurrent events, the analysis uses a counting process approach. Counting Process is an approach used to deal with survival data with identical recurrent events, meaning that recurrences are caused by the same thing, which in this case is the narrowing of the bronchioles in asthmatics. The purpose of this study was to determine the factors that cause asthma exacerbations by using a counting process approach as a data treatment for recurrent events at Diponegoro National Hospital. Based on the results of the analysis, the factors that influence the length of time a patient experiences an exacerbation are the age, gender, and type of cases

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7

Pellerey, Franco. "Shock models by underlying counting process." Journal of Applied Probability 31, no. 1 (1994): 156–66. http://dx.doi.org/10.2307/3215243.

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Suppose that a device is subjected to shocks governed by a counting process N = {N(t), t ≧0}. The probability that the device survives beyond time t is then H̄(t)=Σk=0∞Q̄ℙ[N(t)=k], where Q̄k is the probability of surviving k shocks. It is known that H is NBU if the interarrivals Uk, ∊ ℕ+, are independent and NBU, and Q̄k+j ≦ Q̄k· Q̄j holds whenever k, j ∊ ℕ. Similar results hold for the classes of the NBUE and HNBUE distributions. We show that some other ageing classes have similar properties.

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8

Suydam, Marilyn N. "Research Report: The Process of Counting." Arithmetic Teacher 33, no. 5 (1986): 29. http://dx.doi.org/10.5951/at.33.5.0029.

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Recently, a number of researchers have directed their attention to the process of counting, sometimes in connection with studies of problem solving or of addition and subtraction. The emphasis is on identifying how children learn to count or use counting; however, how the ideas children discover on their own might be applied in instruction is the ultimate goal. For instance, children might be led to discover new ideas by examining their own invented strategies.

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9

Pellerey, Franco. "Shock models by underlying counting process." Journal of Applied Probability 31, no. 01 (1994): 156–66. http://dx.doi.org/10.1017/s0021900200107417.

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Abstract:

Suppose that a device is subjected to shocks governed by a counting process N = {N(t), t ≧0}. The probability that the device survives beyond time t is then H̄(t)=Σk=0 ∞Q̄ℙ[N(t)=k], where Q̄k is the probability of surviving k shocks. It is known that H is NBU if the interarrivals Uk , ∊ ℕ+, are independent and NBU, and Q̄ k+j ≦ Q̄k · Q̄j holds whenever k, j ∊ ℕ. Similar results hold for the classes of the NBUE and HNBUE distributions. We show that some other ageing classes have similar properties.

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10

Klein, John. "Statistical Models Based On Counting Process." Technometrics 36, no. 1 (1994): 111–12. http://dx.doi.org/10.1080/00401706.1994.10485407.

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