CAU Scholar's Space: Lim, Yae Ji

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Lim, Yae Ji (임예지)

대학원 (통계데이터사이언스학과)

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Tel 02-820-5906
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Functional data:5||precipitation:4||prediction:4||Pseudo data:4||clustering:4||ultrasonography:3||metastasis:3||principal component analysis:3||Principal component analysis:3||classification:3||climate change:3||Robustness:3||spectral envelope:2||주성분 분석:2||magnetic resonance imaging:2||SARSCoV2:2||ESalgorithm:2||thyroid neoplasm:2||quantile regression:2||Antibodies:2||independent component analysis:2||regularized regression:2||ROBPCA:2||COVID19:2||Kmeans:2||functional clustering:2||Highdimensional data:2||breast cancer:2||functional principal component analysis:2||Disease severity:1||Dynamic principal component analysis:1||Empirical mode decomposition:1||Erosive esophagitis:1||Extreme distribution:1||Extremes:1||FPCA:1||Factor model:1||Feature extraction:1||Fine particulate matter:1||Health Personnel:1||Hierarchical $K$means clustering:1||Hierarchical $K$means 군집화 알고리즘:1||High dimensional data:1||Hypertension:1||Image enhancement:1||Interferons:1||Korea:1||Laplace crossperiodogram:1||Laplace periodogram:1||Log periodogram regression:1||Longmemory process:1||Magnetic resonance:1||Magnetic resonance imaging:1||Menopausal status:1||Missing segments:1||Multiresolution analysis:1||Multiscale method:1||Multivariate functional principal component analysis:1||Mycobacterium bovis:1||Newly confirmed COVID19 case data:1||Nonstationary time series:1||Oncotype DX:1||Outliers:1||PAM:1||PET:1||PM10 prediction:1||PM2.5 prediction:1||PM_10 concentration:1||PM_10 prediction:1||Penalized quantile regression:1||Penalized spline:1||Platelet aggregation inhibitors:1||Poisson data:1||Postacute COVID19 syndrome:1||Principal component regression:1||Principal expectile component regression:1||Prognosis:1||Proximal femur fracture:1||Quantile:1||Quantile loss:1||Quantile periodogram:1||Radiography:1||Rankbased transform:1||Respiratory function tests:1||Respiratorygated imaging techniques:1||Riemannian functional principal component analysis:1||SCAD penalty:1||Seismic data:1||Simultaneous confidence interval:1||Skewed data:1||Sparsity:1||Spatiotemporal data:1||Step count data:1||Step data:1||Submatrix completion:1||Thick pen transform:1||Thickpen transform:1||Thyroid cancer:1||Thyroid neoplasm:1||Transplantation:1||Tuberculosis:1||Type 1 diabetes mellitus:1||Ultrasonography:1||Vaccination:1||Variable selection:1||WC:1||Wavelet process:1||Wavelet transform:1||Zeroinflated time series data:1||accuracy measures:1||advanced metering infrastructure:1||advanced metering infrastructure (AMI):1||automation:1||binning:1||bootstrap aggregating:1||breast:1||canonical correlation analysis:1||change point detection:1||clustered diagnostic test:1||clustering algorithm:1||clustering validity index:1||combined immunosuppression:1||correct indication of tumor location:1||correlated diagnostic test:1||cumulative sum type statistics:1||data mining:1||dataadaptive principal component analysis:1||diffusion background signal:1||diffusionweighted imaging:1||dynamic factor model:1||dynamic principal component analysis:1||empirical orthogonal function:1||empirical orthogonal functions:1||employment:1||energy usage behavior:1||ensemble clustering:1||extreme value theory:1||follicular thyroid carcinoma:1||frequency:1||frequency domain:1||frequency domain principal component analysis:1||functional data:1||functional data analysis:1||functional magnetic resonance imaging:1||generalized estimating equations:1||graphical models:1||group independent component analysis:1||high dimensional data:1||highdimensional data:1||histogram:1||homocysteine:1||inductively coupled plasmamass spectrometry:1||job clustering:1||job replacement:1||latent Gaussian process:1||left bundlebranch block:1||lesion detectability:1||local wavelet periodogram:1||locally stationary data:1||longmemory process:1||lymph node:1||machine learning:1||machinelearning methods:1||magnetic resonance enterography:1||math altimg="urn:1||math>centres functional clustering:1||media:1||methylmalonic acid:1||mi></mml:1||mi>k</mml:1||missing problem:1||modelbased clustering:1||mucosal healing:1||multivariate functional principal component analysis:1||nonGaussian distribution:1||nonstationary data:1||papillary thyroid carcinoma:1||particulate matter:1||periodogram:1||positive predictive value:1||power consumption data:1||pregnancy:1||principal component regression:1||principal differential analysis:1||progressive electricity tariff system:1||pseudo data:1||quantile function:1||quantiles:1||radiomics:1||regularization:1||robust coherence analysis:1||robustness:1||sea surface temperature:1||semipositive predictive value:1||skewed data:1||sparse data:1||spatial data:1||speckletracking echocardiography:1||spectral analysis:1||spectral density matrix:1||spherevalued functional data:1||spline estimator:1||sta4:1||sta4557:1||sta4557math0001" display="inline"><mml:1||step data:1||temperature prediction:1||thoracic:1||thyroid nodule:1||timedependent frequency domain principal components analysis:1||timeofuse rates.:1||trace elements:1||tuberculosis:1||tumor detection:1||vitamin B12:1||wearable device:1||weighted ability rate of substitution:1||xwiley:1||경험적 직교 함수:1||공간자료:1||군집화 알고리즘:1||군집화 유효성 지수:1||그룹 독립성분분석:1||독립성분분석:1||로버스트성:1||미세먼지:1||부스팅:1||분위 회귀분석:1||예측:1||온도 예측:1||전력 사용량 데이터:1||주성분 회귀분석:1||주파수 공간:1||지능형 검침 인프라:1||<mml:1||해수표층 온도값:1||3D echocardiography:1||ABSI:1||Accelerometer data:1||Advanced metering infrastructure:1||Air pollution:1||Antibody titer:1||Association:1||Asymmetric errors:1||Autoregressive error:1||BMI:1||BRI:1||Background parenchymal enhancement:1||Binomial data:1||Boosting:1||Breast cancer:1||Breath holding:1||Canonical correlation analysis:1||Cardiovascular disease:1||Cholangiopancreatography:1||Clustering:1||Composite asymmetric Huber function:1||Composite quantiles:1||Computed tomography:1||Crossspectrum:1||Data compression:1||Delay:1||Diagnostic procedure:1||Dimension reduction:1||

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