• Nuclear Engineering and Technology
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• v.43 no.3
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• pp.287-300
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• 2011

Counting statistics modified by introducing two dead times in series under a Poisson input distribution are discussed. A previous study examined the two cases of series combinations of nonextended-extended (NE-E) and extended-extended (EE) dead times. The present study investigated the remaining two cases of extended-nonextended (E-NE) and nonextended-nonextended (NE-NE) dead times. For the three time origins of the counting processes - ordinary, equilibrium, and shifted processes - a set of formulae was newly developed from a general formulation and presented for the event time interval densities, total densities, and exact mean and variance of the counts in a given counting duration. The asymptotic expressions for the mean and variance of the counts, which are most convenient for applications, were fully listed. The equilibrium mean count rates distorted by the three dead times in series were newly derived from the information obtained in these studies. An application of the derived formulae is briefly discussed.

• Journal of Radiation Protection and Research
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• v.29 no.4
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• pp.269-273
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• 2004

The hybrid dead time model adopting paralyzable (or extendable) and non-paralyzable (or non-extendable) dead times has been introduced to extend the usable range of G-M counters in high counting rate environment and the relationship between true and observed counting rates is more accurately expressed in the hybrid model. GMSIM, dead time effects simulator, has been developed to analyze the counting statistics of G-M counters using Monte Carlo simulations. GMSIM accurately described the counting statistics of the paralyzable and non-paralyzable models. For G-M counters that follow the hybrid model, the counting statistics behaved in between two idealized models. In the future, GMSIM may be used in predicting counting statistics of three G-M dead time models, which are paralyzable, non-paralyzable and hybrid models.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.859-864
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• 2012

Poisson regression and negative binomial regression are usually used to analyze counting data; however, these models are unsuitable for fit zero-inflated data that contain unexpected zero-valued observations. In this paper, we review the zero-inflated regression in which Bernoulli process and the counting process are hierarchically mixed. It is known that zero-inflated regression can efficiently model the over-dispersion problem. Vuong statistic is employed to compare performances of the zero-inflated models with other standard models.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.281-292
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• 1997

In this paper we are concered with the counting processes with intersity function $g_n(t)$, where $g_n(t)$ not only depends on t but n. It is shown that under certain conditions the number of events in [0, t] follows a generalizes Poisson distribution. A counting process is also provided such that $g_i(t)$$\neq$$g_i(t)$ for i$\neq$j and the number of events in [0, t] has a transformed geometric distribution.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.169-176
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• 2014

Counting processes are widely used in many fields, whose properties are determined by the intensity function. For estimation of the parameters of the intensity functions when the process is observed continuously over a fixed interval, the likelihood function is of interest. However in the literature there are only heuristic derivations and some results are not coincident. We thus in this note derive the likelihood function of the counting process in a rigorous way. So this note fill up a hole in derivation of the likelihood function.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.273-281
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• 1996

It is derived that conditions of counting process ($\{N(t){\mid}t\;{\geq}\;0\}$) in which the number of events in time interval [0, t] has a (n, n+1)-generalized Poisson distribution with parameters (${\theta}t,\;{\lambda}$) and a generalized inflated Poisson distribution with parameters (${\{\lambda}t,\;{\omega}\}$.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.53-77
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• 1994

In a certain stochastic process, Cox's regression model is used to analyze multistate survival data. From this model, the regression parameter vectors, survival functions, and the probability of being in response function are estimated based on multistate Cox's partial likelihood and nonparametric likelihood methods. The asymptotic properties of these estimators are described informally through the counting process approach. An example is given to likelihood the results in this paper.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.195-204
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• 1996

Some rank tests for comparing r treatments against ordered alternatives are proposed when some of data are randomly cemsored, which are the weighted logrank tests based on pairwise-ranking scheme. The covariances of the proposed test statistics are explicitly obtained from the results of the counting process theory and the test procedures are illustrated by a numerical example. Simulation studies are also performed for comparing with the other well-known tests.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.79-90
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• 1992

We consider hazard ratio as a descriptive measure to compare the hazard experience of a treatment group with that of a control group with censored survival data. In this paper, we propose a kernel estimator of hazard ratio. The uniform consistency and asymptotic normality of a kernel estimator are proved by using counting process approach via martingale theory and stochastic integrals.