Jinseog Kim - Academia.edu (original) (raw)
Papers by Jinseog Kim
Communications for Statistical Applications and Methods, 2021
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Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrinkage and selection simultaneou... more Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrinkage and selection simultaneously, as LASSO does, but works on blocks of covariates. That is, the grouped LASSO provides a model where some blocks of regression coefficients are exactly zero. The grouped LASSO is useful when there are meaningful blocks of covariates such as polynomial regression and dummy variables from categorical variables. In this paper, we propose an extension of the grouped LASSO, called ‘Blockwise Sparse Regression’ (BSR). The BSR achieves shrinkage and selection simultaneously on blocks of covariates similarly to the grouped LASSO, but it works for general loss functions including generalized linear models. An efficient computational algorithm is developed and a blockwise standardization method is proposed. Simulation results show that the BSR compromises the ridge and LASSO for logistic regression. The proposed method is illustrated with two datasets.
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The glucose metabolisms and serum lipid are assumed as possible intermediary mechanisms in linkin... more The glucose metabolisms and serum lipid are assumed as possible intermediary mechanisms in linking between breast cancer (BC) and obesity. The current report examines the associations between diabetes mellitus (DM) markers (glucose and insulin) and BC markers (monocyte chemoattractant protein-1 (MCP-1), resistin, adiponectin, leptin). Glucose model shows that mean glucose levels are higher for breast cancer women (P=0.0222) then normal. Mean glucose levels are positively associated with leptin (P<0.0001) and homeostasis model assessment score insulin resistance (HOMA-IR) (P<0.0001), while they are negatively associated with interaction effects HOMA-IR*leptin (P<0.0001) and leptin*adiponectin (P=0.0883). On the other hand, variance of glucose levels is positively associated with HOMA-IR (P<0.0001) and resistin (P=0.0218), while it is negatively associated with leptin (P<0.0001), MCP-1 (P=0.0115). Insulin model shows that mean insulin levels are positively associated wi...
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The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood pers... more The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood perspective, and a Gibbs sampler algorithm is proposed for evaluating the Bayesian bootstrap posterior. The proposed Bayesian bootstrap posterior is shown to be the limit of the nonparametric posteriors with Dirich- let process priors as the prior information vanishes, and to be equivalent to the weighted bootstrap on the observables. A small simulation study shows that the proposed Bayesian bootstrap estimator compares favorably with the nonparametric maximum likelihood estimator; furthermore its asymptotic properties are studied.
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Medicine
Supplemental Digital Content is available in the text Abstract Aged population with comorbidities... more Supplemental Digital Content is available in the text Abstract Aged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain. This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit, use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death. Among 5621 patients, the high CCIS (≥ 3) group showed higher proportion of elderly population and lower plasma hemoglobin and lower lymphocyte and platelet counts. The high CCIS group was an independent risk factor for composite outcome (HR 3.63, 95% CI 2.45–5.37, P < .001) and patient mortality (HR 22.96, 95% CI 7.20–73.24, P < .001). The nomogram showed that CCIS was the most important factor contributing to the prognosis followed by the presence of dyspnea (hazard ratio [HR] 2.88, 95% confidence interval [CI] 2.16–3.83), low body mass index < 18.5 kg/m2 (HR 2.36, CI 1.49–3.75), lymphopenia (<0.8 x109/L) (HR 2.15, CI 1.59–2.91), thrombocytopenia (<150.0 x109/L) (HR 1.29, CI 0.94–1.78), anemia (<12.0 g/dL) (HR 1.80, CI 1.33–2.43), and male sex (HR 1.76, CI 1.32–2.34). The nomogram demonstrated that the CCIS was the most potent predictive factor for patient mortality. The predictive nomogram using CCIS for the hospitalized patients with COVID-19 may help clinicians to triage the high-risk population and to concentrate limited resources to manage them.
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Annals of Medicine
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ABSTRACTAged population with comorbidities demonstrated high mortality rate and severe clinical o... more ABSTRACTAged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain. This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5,621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit (ICU), use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death. Among 5,621 patients, the high CCIS (≥3) group showed higher proportion of elderly population and lower plasma hemoglobin and ...
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We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identifi... more We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identified risk factors associated with high patient mortality using Korean nationwide public database of 5,621 hospitalized patients. The mortality rate and admission rate to intensive care unit were compared between asymptomatic and symptomatic patients. The prediction model for patient mortality was developed through risk factor analysis among asymptomatic patients. The prevalence of asymptomatic COVID-19 infection was 25.8%. The mortality rates were not different between groups (3.3% vs. 4.5%, p=0.17). However, symptomatic patients were more likely to receive ICU care compared to asymptomatic patients (4.1% vs. 1.0%, p<0.0001). The age-adjusted Charlson comorbidity index score (CCIS) was the most potent predictor for patient mortality in asymptomatic patients. The clinicians should predict the risk of death by evaluating age and comorbidities but not the presence of symptoms.Article Summa...
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Journal of Cardiovascular Disease Research
Objectives: The role of Cholesterol and its relationship with some cardiac risk factors for heart... more Objectives: The role of Cholesterol and its relationship with some cardiac risk factors for heart patients are examined in the current report using both Cholesterol level and two cardiac factors modeling. Materials and methods: A real data set of 303 heart patients with 14 study characters are considered in the report. Statistical joint generalized linear models (JGLMs) are considered using both Gamma & Log-normal distributions. Results: It is observed from Cholesterol level modeling that Cholesterol level is higher for female heart patients (P=0.0013) than male, or at older ages (P=0.0012) than younger. It is higher for the patients with high maximum heart rate (P=0.0877), or having resting electrocardiographic at normal level (P=0.0107), or with thalassemia at reversal defect (P=0.0466) and at fixed defect (P=0.0940) than at normal. It is also higher for the patients having heart disease diagnosis (angiographic disease status) value 0 (meaning less than 50% diameter narrowing) (P=0.0515) than others. Variance of Cholesterol level is higher for female patients (P=0.0265) than male, and it increases as ST depression induced by exercise relative to rest (Oldpeak) (P=0.0095) increases. From maximum heart rate modeling, it is noted that maximum heart rate increases as the Cholesterol level (P=0.0325) increases. In addition, variance of maximum heart rate decreases as the Cholesterol level (P=0.0058) increases. Also from resting blood pressure modeling, it is observed that mean resting blood pressure increases as the Cholesterol level increases, where it is a confounder in the model. Conclusions: Cholesterol levels should be examined regularly at older ages, along with the maximum heart rate achieved, thalassemia status, and resting blood pressure for both male and female heart patients.
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Neurocomputing
Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifie... more Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with our semantic tensor space model for document representation. In our work, we intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups, and OHSUMED corpora, we prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance.
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Model Assisted Statistics and Applications, 2017
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ABSTRACT
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The KIPS Transactions:PartC, 2009
The leaking of personal information is mostly occurred by internal users. The confidential inform... more The leaking of personal information is mostly occurred by internal users. The confidential information such as credit card number can be disclosed or modified by system manager easily. The secure storaging and managing scheme for sensitive data of individual and enterprise is required for distributed data management. The manager owning private data is needed to have a weight which is a right to disclose a private data. For deciding a weight, it is required that system is able to designate the level of user`s right. In this paper, we propose the new algorithm named digit-independent algorithm. And we propose a new data management scheme of gathering and processing the data based on digit-independent algorithm. Our sharing and recovering scheme have the efficient computation operation for managing a large quantity of data using weight table. The proposed scheme is able to use for secure e-business data management and storage in ubiquitous computing environment.
Bookmarks Related papers MentionsView impact
Communications of the Korean statistical society, 2012
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Statistica Sinica
The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood pers... more The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood perspective, and a Gibbs sampler algorithm is proposed for evaluating the Bayesian bootstrap posterior. The proposed Bayesian bootstrap posterior is shown to be the limit of the nonparametric posteriors with Dirich-let process priors as the prior information vanishes, and to be equivalent to the weighted bootstrap on the observables. A small simulation study shows that the proposed Bayesian bootstrap estimator compares favorably with the nonparametric maximum likelihood estimator; furthermore its asymptotic properties are studied.
Bookmarks Related papers MentionsView impact
Statistica Sinica
Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrink-age and selection simultaneo... more Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrink-age and selection simultaneously, as LASSO does, but works on blocks of covariates. That is, the grouped LASSO provides a model where some blocks of regression co-efficients are exactly zero. The grouped LASSO is useful when there are meaningful blocks of covariates such as polynomial regression and dummy variables from cat-egorical variables. In this paper, we propose an extension of the grouped LASSO, called 'Blockwise Sparse Regression' (BSR). The BSR achieves shrinkage and se-lection simultaneously on blocks of covariates similarly to the grouped LASSO, but it works for general loss functions including generalized linear models. An efficient computational algorithm is developed and a blockwise standardization method is proposed. Simulation results show that the BSR compromises the ridge and LASSO for logistic regression. The proposed method is illustrated with two datasets.
Bookmarks Related papers MentionsView impact
Reliability Engineering & System Safety, 2015
ABSTRACT In quality engineering, the most commonly used lifetime distributions are log-normal, ex... more ABSTRACT In quality engineering, the most commonly used lifetime distributions are log-normal, exponential, gamma and Weibull. Experimental designs are useful for predicting the optimal operating conditions of the process in lifetime improvement experiments. In the present article, invariant robust first-order D-optimal designs are derived for correlated lifetime responses having the above four distributions. Robust designs are developed for some correlated error structures. It is shown that robust first-order D-optimal designs for these lifetime distributions are always robust rotatable but the converse is not true. Moreover, it is observed that these designs depend on the respective error covariance structure but are invariant to the above four lifetime distributions. This article generalizes the results of Das and Lin [7] for the above four lifetime distributions with general (intra-class, inter-class, compound symmetry, and tri-diagonal) correlated error structures.
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Annual Research & Review in Biology, 2015
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Communications for Statistical Applications and Methods, 2021
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrinkage and selection simultaneou... more Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrinkage and selection simultaneously, as LASSO does, but works on blocks of covariates. That is, the grouped LASSO provides a model where some blocks of regression coefficients are exactly zero. The grouped LASSO is useful when there are meaningful blocks of covariates such as polynomial regression and dummy variables from categorical variables. In this paper, we propose an extension of the grouped LASSO, called ‘Blockwise Sparse Regression’ (BSR). The BSR achieves shrinkage and selection simultaneously on blocks of covariates similarly to the grouped LASSO, but it works for general loss functions including generalized linear models. An efficient computational algorithm is developed and a blockwise standardization method is proposed. Simulation results show that the BSR compromises the ridge and LASSO for logistic regression. The proposed method is illustrated with two datasets.
Bookmarks Related papers MentionsView impact
The glucose metabolisms and serum lipid are assumed as possible intermediary mechanisms in linkin... more The glucose metabolisms and serum lipid are assumed as possible intermediary mechanisms in linking between breast cancer (BC) and obesity. The current report examines the associations between diabetes mellitus (DM) markers (glucose and insulin) and BC markers (monocyte chemoattractant protein-1 (MCP-1), resistin, adiponectin, leptin). Glucose model shows that mean glucose levels are higher for breast cancer women (P=0.0222) then normal. Mean glucose levels are positively associated with leptin (P<0.0001) and homeostasis model assessment score insulin resistance (HOMA-IR) (P<0.0001), while they are negatively associated with interaction effects HOMA-IR*leptin (P<0.0001) and leptin*adiponectin (P=0.0883). On the other hand, variance of glucose levels is positively associated with HOMA-IR (P<0.0001) and resistin (P=0.0218), while it is negatively associated with leptin (P<0.0001), MCP-1 (P=0.0115). Insulin model shows that mean insulin levels are positively associated wi...
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood pers... more The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood perspective, and a Gibbs sampler algorithm is proposed for evaluating the Bayesian bootstrap posterior. The proposed Bayesian bootstrap posterior is shown to be the limit of the nonparametric posteriors with Dirich- let process priors as the prior information vanishes, and to be equivalent to the weighted bootstrap on the observables. A small simulation study shows that the proposed Bayesian bootstrap estimator compares favorably with the nonparametric maximum likelihood estimator; furthermore its asymptotic properties are studied.
Bookmarks Related papers MentionsView impact
Medicine
Supplemental Digital Content is available in the text Abstract Aged population with comorbidities... more Supplemental Digital Content is available in the text Abstract Aged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain. This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit, use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death. Among 5621 patients, the high CCIS (≥ 3) group showed higher proportion of elderly population and lower plasma hemoglobin and lower lymphocyte and platelet counts. The high CCIS group was an independent risk factor for composite outcome (HR 3.63, 95% CI 2.45–5.37, P < .001) and patient mortality (HR 22.96, 95% CI 7.20–73.24, P < .001). The nomogram showed that CCIS was the most important factor contributing to the prognosis followed by the presence of dyspnea (hazard ratio [HR] 2.88, 95% confidence interval [CI] 2.16–3.83), low body mass index < 18.5 kg/m2 (HR 2.36, CI 1.49–3.75), lymphopenia (<0.8 x109/L) (HR 2.15, CI 1.59–2.91), thrombocytopenia (<150.0 x109/L) (HR 1.29, CI 0.94–1.78), anemia (<12.0 g/dL) (HR 1.80, CI 1.33–2.43), and male sex (HR 1.76, CI 1.32–2.34). The nomogram demonstrated that the CCIS was the most potent predictive factor for patient mortality. The predictive nomogram using CCIS for the hospitalized patients with COVID-19 may help clinicians to triage the high-risk population and to concentrate limited resources to manage them.
Bookmarks Related papers MentionsView impact
Annals of Medicine
Bookmarks Related papers MentionsView impact
ABSTRACTAged population with comorbidities demonstrated high mortality rate and severe clinical o... more ABSTRACTAged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain. This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5,621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit (ICU), use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death. Among 5,621 patients, the high CCIS (≥3) group showed higher proportion of elderly population and lower plasma hemoglobin and ...
Bookmarks Related papers MentionsView impact
We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identifi... more We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identified risk factors associated with high patient mortality using Korean nationwide public database of 5,621 hospitalized patients. The mortality rate and admission rate to intensive care unit were compared between asymptomatic and symptomatic patients. The prediction model for patient mortality was developed through risk factor analysis among asymptomatic patients. The prevalence of asymptomatic COVID-19 infection was 25.8%. The mortality rates were not different between groups (3.3% vs. 4.5%, p=0.17). However, symptomatic patients were more likely to receive ICU care compared to asymptomatic patients (4.1% vs. 1.0%, p<0.0001). The age-adjusted Charlson comorbidity index score (CCIS) was the most potent predictor for patient mortality in asymptomatic patients. The clinicians should predict the risk of death by evaluating age and comorbidities but not the presence of symptoms.Article Summa...
Bookmarks Related papers MentionsView impact
Journal of Cardiovascular Disease Research
Objectives: The role of Cholesterol and its relationship with some cardiac risk factors for heart... more Objectives: The role of Cholesterol and its relationship with some cardiac risk factors for heart patients are examined in the current report using both Cholesterol level and two cardiac factors modeling. Materials and methods: A real data set of 303 heart patients with 14 study characters are considered in the report. Statistical joint generalized linear models (JGLMs) are considered using both Gamma & Log-normal distributions. Results: It is observed from Cholesterol level modeling that Cholesterol level is higher for female heart patients (P=0.0013) than male, or at older ages (P=0.0012) than younger. It is higher for the patients with high maximum heart rate (P=0.0877), or having resting electrocardiographic at normal level (P=0.0107), or with thalassemia at reversal defect (P=0.0466) and at fixed defect (P=0.0940) than at normal. It is also higher for the patients having heart disease diagnosis (angiographic disease status) value 0 (meaning less than 50% diameter narrowing) (P=0.0515) than others. Variance of Cholesterol level is higher for female patients (P=0.0265) than male, and it increases as ST depression induced by exercise relative to rest (Oldpeak) (P=0.0095) increases. From maximum heart rate modeling, it is noted that maximum heart rate increases as the Cholesterol level (P=0.0325) increases. In addition, variance of maximum heart rate decreases as the Cholesterol level (P=0.0058) increases. Also from resting blood pressure modeling, it is observed that mean resting blood pressure increases as the Cholesterol level increases, where it is a confounder in the model. Conclusions: Cholesterol levels should be examined regularly at older ages, along with the maximum heart rate achieved, thalassemia status, and resting blood pressure for both male and female heart patients.
Bookmarks Related papers MentionsView impact
Neurocomputing
Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifie... more Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with our semantic tensor space model for document representation. In our work, we intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups, and OHSUMED corpora, we prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance.
Bookmarks Related papers MentionsView impact
Model Assisted Statistics and Applications, 2017
Bookmarks Related papers MentionsView impact
ABSTRACT
Bookmarks Related papers MentionsView impact
The KIPS Transactions:PartC, 2009
The leaking of personal information is mostly occurred by internal users. The confidential inform... more The leaking of personal information is mostly occurred by internal users. The confidential information such as credit card number can be disclosed or modified by system manager easily. The secure storaging and managing scheme for sensitive data of individual and enterprise is required for distributed data management. The manager owning private data is needed to have a weight which is a right to disclose a private data. For deciding a weight, it is required that system is able to designate the level of user`s right. In this paper, we propose the new algorithm named digit-independent algorithm. And we propose a new data management scheme of gathering and processing the data based on digit-independent algorithm. Our sharing and recovering scheme have the efficient computation operation for managing a large quantity of data using weight table. The proposed scheme is able to use for secure e-business data management and storage in ubiquitous computing environment.
Bookmarks Related papers MentionsView impact
Communications of the Korean statistical society, 2012
Bookmarks Related papers MentionsView impact
Statistica Sinica
The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood pers... more The Bayesian bootstrap for doubly censored data is constructed from the empirical likelihood perspective, and a Gibbs sampler algorithm is proposed for evaluating the Bayesian bootstrap posterior. The proposed Bayesian bootstrap posterior is shown to be the limit of the nonparametric posteriors with Dirich-let process priors as the prior information vanishes, and to be equivalent to the weighted bootstrap on the observables. A small simulation study shows that the proposed Bayesian bootstrap estimator compares favorably with the nonparametric maximum likelihood estimator; furthermore its asymptotic properties are studied.
Bookmarks Related papers MentionsView impact
Statistica Sinica
Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrink-age and selection simultaneo... more Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrink-age and selection simultaneously, as LASSO does, but works on blocks of covariates. That is, the grouped LASSO provides a model where some blocks of regression co-efficients are exactly zero. The grouped LASSO is useful when there are meaningful blocks of covariates such as polynomial regression and dummy variables from cat-egorical variables. In this paper, we propose an extension of the grouped LASSO, called 'Blockwise Sparse Regression' (BSR). The BSR achieves shrinkage and se-lection simultaneously on blocks of covariates similarly to the grouped LASSO, but it works for general loss functions including generalized linear models. An efficient computational algorithm is developed and a blockwise standardization method is proposed. Simulation results show that the BSR compromises the ridge and LASSO for logistic regression. The proposed method is illustrated with two datasets.
Bookmarks Related papers MentionsView impact
Reliability Engineering & System Safety, 2015
ABSTRACT In quality engineering, the most commonly used lifetime distributions are log-normal, ex... more ABSTRACT In quality engineering, the most commonly used lifetime distributions are log-normal, exponential, gamma and Weibull. Experimental designs are useful for predicting the optimal operating conditions of the process in lifetime improvement experiments. In the present article, invariant robust first-order D-optimal designs are derived for correlated lifetime responses having the above four distributions. Robust designs are developed for some correlated error structures. It is shown that robust first-order D-optimal designs for these lifetime distributions are always robust rotatable but the converse is not true. Moreover, it is observed that these designs depend on the respective error covariance structure but are invariant to the above four lifetime distributions. This article generalizes the results of Das and Lin [7] for the above four lifetime distributions with general (intra-class, inter-class, compound symmetry, and tri-diagonal) correlated error structures.
Bookmarks Related papers MentionsView impact
Annual Research & Review in Biology, 2015
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