• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.265-278
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• 1998

In this paper, we suggest two-phase adaptive cluster sampling schemes. The main feature of the two-phase sampling is that the information collected in the first phase sample is utilized in the selection of the second phase sample. The conventional two-phase sampling is, however, not sufficient to increase efficiency when the population of interest is rare and clustered. In the proposed sampling scheme, the first phase sample is selected with adaptive cluster sampling procedure and the second phase sample is selected by PPSWR and $\pi$PS sampling. We investigate unbiased estimators of population total and their variance for the proposed sampling schemes respectively. Finally we compare these suggested sampling schemes using numerical examples .

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.479-489
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• 2002

The case which applies two-phase sampling to stratification and nonresponse problem, it is a poweful and effective technique. In this paper we study the calibration estimator and its variance estimator for the population total using two-phase sampling method according to the of auxiliary information for population and sample having strong correlation with an interested variable in unit nonresponse situation. The auxiliary information that available both at first-phase and second-phase sampling can be used to improve weights by the calibration procedure. A weight which corresponds to the product of sampling weights and response probability is calculated at each phase of sampling.

• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1219-1224
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• 2018

We investigate the evolutionary dynamics and the phase transitions of the island model which consists of subdivided populations of individuals confined to two islands. In the island model, the population is subdivided so that migration acts to determine the evolutionary dynamics along with selection and genetic drift. The individuals are assumed to be haploid and to be one of two species, X or Y. They reproduce according to their fitness values, die at random, and migrate between the islands. The evolutionary dynamics of an individual based model is formulated in terms of a master equation and is approximated by using the diffusion method as the multidimensional Fokker-Planck equation (FPE) and the coupled non-linear stochastic differential equations (SDEs) with multiplicative noise. We analyze the infinite population limit to find the phase transitions from the monomorphic state of one type to the polymorphic state to the monomorphic state of the other type as we vary the ratio of the fitness values in two islands and complete the phase diagram of our island model.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.165-181
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• 2010

This paper presents some chain ratio-type estimators for estimating finite population variance using two auxiliary variables in two phase sampling set up. The expressions for biases and mean squared errors of the suggested c1asses of estimators are given. Asymptotic optimum estimators(AOE's) in each class are identified with their approximate mean squared error formulae. The theoretical and empirical properties of the suggested classes of estimators are investigated. In the simulation study, we took a real dataset related to pulmonary disease available on the CD with the book by Rosner, (2005).

• The Plant Pathology Journal
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• v.31 no.1
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• pp.33-40
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• 2015

Pigeonpea Sterility Mosaic Disease (PSMD) is an important foliar disease caused by Pigeonpea sterility mosaic virus (PPSMV) which is transmitted by eriophyid mites (Aceria cajani Channabasavanna). In present study, a F2 mapping population comprising 325 individuals was developed by crossing PSMD susceptible genotype (Gullyal white) and PSMD resistant genotype (BSMR 736). We identified a set of 32 out of 300 short decamer random DNA markers that showed polymorphism between Gullyal white and BSMR 736 parents. Among them, eleven DNA markers showed polymorphism including coupling and repulsion phase type of polymorphism across the parents. Bulked Segregant Analysis (BSA), revealed that the DNA marker, IABTPPN7, produced a single coupling phase marker (IABTPPN $7_{414}$) and a repulsion phase marker (IABTPPN $7_{983}$) co-segregating with PSMD reaction. Screening of 325 F2 population using IABTPPN7 revealed that the repulsion phase marker, IABTPPN $7_{983}$, was co-segregating with the PSMD responsive SV1 at a distance of 23.9 cM for Bidar PPSMV isolate. On the other hand, the coupling phase marker IABTPPN $7_{414}$ did not show any linkage with PSMD resistance. Additionally, single marker analysis both IABTPPN $7_{983}$ (P<0.0001) and IABTPPN $7_{414}$ (P<0.0001) recorded a significant association with the PSMD resistance and explained a phenotypic variance of 31 and 36% respectively in $F_2$ population. The repulsion phase marker, IABTPPN7983, could be of use in Marker-Assisted Selection (MAS) in the PPSMV resistance breeding programmes of pigeonpea.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.387-402
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• 2009

In this paper we have considered the problem of estimating the population mean $\bar{Y}$ of the study variable y using auxiliary information in presence of non-response. Classes of estimators for $\bar{Y}$ in the presence of non-response on the study variable y only and complete response on the auxiliary variable x is available, have been proposed in different situations viz., (i) population mean $\bar{X}$ is known, (ii) when population mean $\bar{X}$ and variance $S^2_x$ are known; (iii) when population mean $\bar{X}$ is not known: and (iv) when both population mean $\bar{X}$ and variance $S^2_x$ are not known: single and two-phase (or double) sampling. It has been shown that various estimators including usual unbiased estimator and the estimators reported by Rao (1986), Khare and Srivastava (1993, 1995) and Tabasum and Khan (2006) are members of the proposed classes of estimators. The optimum values of the first phase sample size n', second phase sample size n and the sub sampling fraction 1/k have been obtained for the fixed cost and the fixed precision. To illustrate foregoing, we have carried out an empirical investigation to reflect the relative performance of all the potentially competing estimators including the one due to Hansen and Hurwitz (1946) estimator, Rao (1986) estimator, Khare and Srivastava (1993, 1995) and Tabasum and Khan (2006) estimator.

• Korean Journal of Optics and Photonics
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• v.8 no.5
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• pp.357-361
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• 1997

We report the operation of a self-starting phase conjugate laser(PCL) oscillator which compensates intracavity phase distortion. The self-starting PCL in the population inverted Nd:YAG gain media produced an output energy of 200 mJ in a 20 ns single-longitudinal-mode pulse at 10Hz. And it showed well-defined Gaussian spatial profile.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1425-1433
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• 1999

In this Paper, we analyzed the HIPERLAN(Hlgh PErformance Radio LAN) MAC(Medium Access Control) protocol, which was a standard for Wireless LANs defined by ETSI. We analyzed mathematically the elimination phase and the yield phase of the channel contention phase of CAC(Channel Access Control) layer in the infinite population model of the HIPERLAN using EY-NPMA(Elimination Yield-Nonpreemptive Priority Multiple Access)protocol. Also we analyzed the probability of successful transmission in the transmission phase. And, we proved results of a mathematical analysis by the simulation of HIPERLAN MAC protocol.

• Korean journal of applied entomology
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• v.42 no.4
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• pp.367-381
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• 2003

Dispersal polymorphism in insects Is a kind of adaptive strategy of the life history together with the diapause, consisting of the “long-winged or alate forms” of migratory phase and the “short-winged or apterous forms” of stationary phase. Dispersal polymorphism is a polymorphism related with the flight capability, and has three categories ; the wing polymorphisms, flight muscle polymorphisms, and flight behavior variations. Phase variation is another type of dispersal polymorphism varying in morphology, physiology and wing forms in response to the density of the population. The dispersal migration is a very adaptive trait that enables a species to keep pace with the changing mosaic of its habitat, but requires some costs. In general, wing reduction has a positive effect on the reproductive potential such as earlier reproduction and larger fecundity The dispersal polymorphism is a kind of optimization in the evolutionary strategies of the life history in insects; a trade-off between the advantages and disadvantages of migration. Wing polymorphism is a phenotypically plastic trait. Wing form changes with the environmental conditions even though the species is the same. Various environmental factors have an effect on the dispersal polymorphisms. Density dependent dispersal polymorphism plays an important role In population dynamics, but it is not a simple function of the density; the individuals of a population may be different in response to the density resulting different outcomes in the population biology, and the detailed information on the genotypic variation of the individuals in the population is the fundamental importance in the prediction of the population performances in a given environment. In conclusion, the studies on the dispersal polymorphisms are a complicated field in relation with both physiology and ecology, and studies on the ecological and quantitative genetics have indeed contributed to understanding of its important nature. But the final factors of evolution; the mechanisms of natural selections, might be revealed through the studies on the population biology.

• Korean Journal of Clinical Pharmacy
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• v.26 no.4
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• pp.312-317
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• 2016

Objective: Midazolam is mainly metabolized by cytochrome P450 (CYP) 3A. Inhibition or induction of CYP3A can affect the pharmacological activity of midazolam. The aims of this study were to develop a population pharmacokinetic (PK) model and evaluate the effect of CYP3A-mediated interactions among ketoconazole, rifampicin, and midazolam. Methods: Three-treatment, three-period, crossover study was conducted in 24 healthy male subjects. Each subject received 1 mg midazolam (control), 1 mg midazolam after pretreatment with 400 mg ketoconazole once daily for 4 days (CYP3A inhibition phase), and 2.5 mg midazolam after pretreatment with 600 mg rifampicin once daily for 10 days (CYP3A induction phase). The population PK analysis was performed using a nonlinear mixed effect model ($NONMEM^{(R)}$ 7.2) based on plasma midazolam concentrations. The PK model was developed, and the first-order conditional estimation with interaction was applied for the model run. A three-compartment model with first-order elimination described the PK. The influence of ketoconazole and rifampicin, CYP3A5 genotype, and demographic characteristics on PK parameters was examined. Goodness-of-fit (GOF) diagnostics and visual predictive checks, as well as bootstrap were used to evaluate the adequacy of the model fit and predictions. Results: Twenty-four subjects contributed to 900 midazolam concentrations. The final parameter estimates (% relative standard error, RSE) were as follows; clearance (CL), 31.8 L/h (6.0%); inter-compartmental clearance (Q) 2, 36.4 L/h (9.7%); Q3, 7.37 L/h (12.0%), volume of distribution (V) 1, 70.7 L (3.6%), V2, 32.9 L (8.8%); and V3, 44.4 L (6.7%). The midazolam CL decreased and increased to 32.5 and 199.9% in the inhibition and induction phases, respectively, compared to that in control phase. Conclusion: A PK model for midazolam co-treatment with ketoconazole and rifampicin was developed using data of healthy volunteers, and the subject's CYP3A status influenced the midazolam PK parameters. Therefore, a population PK model with enzyme-mediated drug interactions may be useful for quantitatively predicting PK alterations.