• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.54.1-54.1
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• 2015

We report results of investigation of amplitude calibration for very long baseline interferometry (VLBI) observations with Korean VLBI Network (KVN). Amplitude correction factors are estimated based on comparison of KVN observations at 22 GHz correlated by Daejeon hardware correlator and DiFX software correlator in Korea Astronomy and Space Science Institue (KASI) with Very Long Baseline Array (VLBA) observations at 22 GHz by DiFX software correlator in National Radio Astronomy Observatory (NRAO). We used the observations for compact radio sources, 3C 454.3 and NRAO 512 which are almost unresolved for baselines in a range of 350-477 km. VLBA visibility data of the sources observed with similar baselines as KVN are selected, fringe-fitted, calibrated, and compared in their amplitudes. We found that visibility amplitudes of KVN observations should be corrected by factors of 1.14 and 1.40 when correlated by DiFX and Daejeon correlators, respectively. These correction factors are attributed to the combination of two steps of 2-bit quantization in KVN observing systems and characteristics of Daejeon correlator.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.30.2-30.2
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• 2008

The Cosmic Far-Infrared Background (CFIRB) contains information about the number and distribution of contributing sources and thus gives us an important key to understand the evolution of galaxies. In order to detect CFIRB fluctuation effectively, we have to analyze the confusion carefully which sets a fundamental limit to the deep observations. From our deep observations, we can compare the background fluctuation via observations of regions at different Galactic latitudes. Our comparative study between estimated confusion levels from our observations and those from our model enables us to understand the nature of CFIRB. We introduce our CFIRB observations and report the preliminary results.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.197-212
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• 2002

When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.73-79
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• 2010

Missing values for unit root processes are imputed by the most recent observations. Treating the imputed observations as if they are complete ones, semiparametric unit root tests are extended to missing value situations. Also, an invariance principle for the partial sum process of the imputed observations is established under some mild conditions, which shows that the extended tests have the same limiting null distributions as those based on complete observations. The proposed tests are illustrated by analyzing an unequally spaced real data set.

• Wind and Structures
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• v.19 no.3
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• pp.271-294
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• 2014

The impact of artefacts in archived wind observations on the design wind speed obtained by extreme value analysis is demonstrated using case studies. A signpost protocol for detecting candidate artefacts is described and its performance assessed by comparing results against previously validated data. The protocol targets artefacts by exploiting the serial correlation between observations. Additional "sieve" algorithms are proposed to identify types of correctable artefact from their "signature" in the data. In extreme value analysis, artefacts displace valid observations only when they are larger, hence always increase the design wind speed. Care must be taken not identify large valid values as artefacts, since their removal will tend to underestimate the design wind speed.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.489-498
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• 1998

The influence of observations the on the goodness-of-fit test in maximum likelihood factor analysis is investigated by using the local influence method. under an appropriate perturbation the test statistic forms a surface. One of main diagnostics is the maximum slope of the perturbed surface the other is the direction vector cor-responding to the curvature. These influence measures provide the information about jointly influence measures provide the information about jointly influential observations as well as individ-ually influential observations.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.60.1-60.1
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• 2019

In this talk, I will briefly review the discovery and early-time interferometric observations of the Galactic Center radio source and then go through recent major updates including the improvement in the array capabilities, developments of the scattering and intrinsic structure modeling, and high-frequency astrometric observations of this source. Introduction of the 1.3 mm VLBI observations in 2017 and 2018 (e.g. array configuration and signal-to-noise ratios), as well as the related multi-wavelength campaign (including GMVA and EAVN observations), will be also presented.

• Atmosphere
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• v.23 no.2
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• pp.171-186
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• 2013

Recently, the number of observations used in a data assimilation system is increasing due to the enormous amount of observations, including satellite data. However, it is not clear that all of these observations are always beneficial to the performance of the numerical weather prediction (NWP). Therefore, it is important to evaluate the effect of observations on these forecasts so that the observations can be used more usefully in NWP process. In this study, the adjoint-based Forecast Sensitivity to Observation (FSO) method with the KMA Unified Model (UM) is applied to two high-impact weather events which occurred in summer and winter in Korea in an effort to investigate the effects of observations on the forecasts of these events. The total dry energy norm is used as a response function to calculate the adjoint sensitivity. For the summer case, TEMP observations have the greatest total impact while BOGUS shows the greatest impact per observation for all of the 24-, 36-, and 48-hour forecasts. For the winter case, aircraft, ATOVS, and ESA have the greatest total impact for the 24-, 36-, and 48-hour forecasts respectively, while ESA has the greatest impact per observation. Most of the observation effects are horizontally located upwind or in the vicinity of the Korean peninsula. The fraction of beneficial observations is less than 50%, which is less than the results in previous studies. As an additional experiment, the total moist energy norm is used as a response function to measure the sensitivity of 24-hour forecast error to observations. The characteristics of the observation impact with the moist energy response function are generally similar to those with the dry energy response function. However, the ATOVS observations were found to be sensitive to the response function, showing a positive (a negative) effect on the forecast when using the dry (moist) norm for the summer case. For the winter case, the dry and moist energy norm experiments show very similar results because the adjoint of KMA UM does not calculate the specific humidity of ice properly such that the dry and moist energy norms are very similar except for the humidity in air that is very low in winter.

• Journal of Environmental Science International
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• v.15 no.8
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• pp.759-774
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• 2006

We employed two data assimilation techniques including MM5 Four Dimensional Data Asssimilation (FDDA) and Local Analysis and Prediction System (LAPS) to find out the effects of the changed inetial conditions on the wind fields simulation according to the objective analysis methods. We designed 5 different modeling cases. EXP B used no data assimilation system. Both EXP Fl using surface observations and EXP F2 with surface and upper-air observations employed MM5 FDDA. EXP Ll using surface observations and EXP L2 with surface and upper-air observations used LAPS. As results of, simulated wind fields using MM5 FDDA showed locally characterized wind features due to objective analysis techniques in FDDA which is forcefully interpolating simulated results into observations. EXP Fl represented a large difference in comparison of wind speed with EXP B. In case of LAPS, simulated horizontal distribution of wind fields showed a good agreement with the patterns of initial condition and EXP Ll showed comparably lesser effects of data assimilation of surface observations than EXP Fl. When upper-air observations are applied to the simulations, while MM5 FDDA could hardly have important effects on the wind fields simulation and showed little differences with simulations with merely surface observations (EXP Fl), LAPS played a key role in simulating wind fields accurately and it could contribute to alleviate the over-estimated winds in EXP Ll simulations.