• Journal of the Korean Mathematical Society
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• v.47 no.6
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• pp.1137-1146
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• 2010

In this paper, we study the divergence based goodness of fit test for partially observed sample from diffusion processes. In order to derive the limiting distribution of the test, we study the asymptotic behavior of the residual empirical process based on the observed sample. It is shown that the residual empirical process converges weakly to a Brownian bridge and the associated phi-divergence test has a chi-square limiting null distribution.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.707-711
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• 2009

We propose new cointegration tests using signs of the regressors as instrumental variable. Our tests have the asymptotic standard normal distribution and are free from the dimension of regressors under the null hypothesis of no cointegration. A Monte-Carlo simulation shows that the proposed tests have a stable size and an improved power. Particulary, the tests have better power for small numbers of observations.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.851-859
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• 2006

The family income distributions of NLSY97 and CPS youth data are compared by using the generalized beta distribution of the second kind. The null hypothesis that the two data sets represent the same underlying population is rejected. The ML estimation suggests that NLSY97 data are oversampled in an income group of $11,308 or less, by about 15.7% compared to CPS data.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.889-902
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• 2013

Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)'s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.6 s.360
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• pp.57-63
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• 2007

In this paper, a new approach to the optimum synthesis of line source difference patterns is proposed for the monopulse tracking way antennas. In the Proposed scheme, which is different from the well-known Bayliss difference pattern synthesis, the difference patterns with the desired individual sidelobe levels are optimally synthesized by appropriately modifying the Taylor line source sum pattern formula. That is, the relationship between the difference pattern and the corresponding source distribution function is analytically established, and then the desired pattern and the distribution are simultaneously extracted by the optimum perturbation of pattern null positions. Furthermore this method provides more rapid sidelobe decay rates than conventional methods. Some numerical results show the validity and usefulness of the proposed procedures.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.639-646
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• 2015

We propose simultaneous tests for mean and variance under the normality assumption. After formulating the null hypothesis and its alternative, we construct test statistics based on the individual p-values for the partial tests with combining functions and derive the null distributions for the combining functions. We then illustrate our procedure with industrial data and compare the efficiency among the combining functions with individual partial ones by obtaining empirical powers through a simulation study. A discussion then follows on the intersection-union test with a combining function and simultaneous confidence region as a simultaneous inference; in addition, we discuss weighted functions and applications to the statistical quality control. Finally we comment on nonparametric simultaneous tests.

• Journal of Computing Science and Engineering
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• v.4 no.4
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• pp.276-290
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• 2010

The join operator is fundamental in relational database systems. Evaluating join queries on large tables is challenging because records need to be efficiently matched based on a given key. In this work, we analyze join queries in SQL with large tables in which a foreign key may be null, invalid or valid, given a referential integrity constraint. We conduct an extensive join performance evaluation on three DBMSs. Specifically, we study join queries varying table sizes, row size and key probabilistic distribution, inserting null, invalid or valid foreign key values. We also benchmark three well-known query optimizations: view materialization, secondary index and join reordering. Our experiments show certain optimizations perform well across DBMSs, whereas other optimizations depend on the DBMS architecture.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.401-409
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• 2003

We consider a particle walking on the nonnegative integers and each unit of time it makes, given it is at site k, either a jump of size m distance units to the right with probability $p_{k}$ or it goes back (falls down) to its starting point 0, a retaining barrier, with probability $v_{k}\;=\;1\;-\;p_{k}$. This is a Markov chain on the integers $mZ^{+}$. We show that if $v_{k}$ has a nonzero limit, then the Markov chain is positive recurrent. However, if $v_{k}$ speeds to 0, then we may get transient Markov chain. A critical speeding rate to zero is identified to get transience, null recurrence, and positive recurrence. Another type of random walk on $Z^{+}$ is considered in which a particle moves m distance units to the right or 1 distance unit to left with probabilities $p_{k}\;and\;q_{k}\;=\;1\;-\;p_{k}$, respectively. A necessary condition to having a stationary distribution and positive recurrence is obtained.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.199-210
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• 1997

Using regression methods based on quasi-likelihood equation, one only needs to specify the conditional mean and variance functions for the response variable in the analysis. In this paper, an omnibus lack-of-fit test is proposed to test the validity of these two functions. Our test is consistent against the alternative under which either the mean or the variance is not the one specified in the null hypothesis. The large-sample null distribution of our test statistics can be approximated through simulations. Extensive numerical studies are performed to demonstrate that the new test preserves the prescribed type I error probability. Power comparisons are conducted to show the advantage of the new proposal.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.121-135
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• 2015

We consider testing equality of mean functions from two samples of functional data. A novel test based on the adaptive Neyman methodology applied to the Hotelling's T-squared statistic is proposed. Under the enlarged null hypothesis that the distributions of the two populations are the same, randomization methods are proposed to find a null distribution which gives accurate significance levels. An extensive simulation study is presented which shows that the proposed test works very well in comparison with several other methods under a variety of alternatives and is one of the best methods for all alternatives, whereas the other methods all show weak power at some alternatives. An application to a real-world data set demonstrates the applicability of the method.