https://BolinWu-Gridea.github.ioBolin Wu2023-11-22T14:48:10.785Zhttps://github.com/jpmonette/feedData Science Blog
https://BolinWu-Gridea.github.io/images/avatar.pnghttps://BolinWu-Gridea.github.io/favicon.icoAll rights reserved 2023, Bolin Wu<![CDATA[Survival Analysis 5: Accelerated Failure-time (AFT) model]]>https://BolinWu-Gridea.github.io/post/survival-analysis-5-accelerated-failure-time-aft-model/2023-11-22T07:38:12.000ZIf we want to find the relationship between hazard function and other variables, we can use the Cox proportional hazards (PH) model. However, what if we want to model the survival time itself? In this case we can use Accelerated Failure-time (AFT) models.
]]>If we want to find the relationship between hazard function and other variables, we can use the Cox proportional hazards (PH) model. However, what if we want to model the survival time itself? In this case we can use Accelerated Failure-time (AFT) models.
]]><![CDATA[Survival Analysis 4: Cox proportional hazards model]]>https://BolinWu-Gridea.github.io/post/survival-analysis-4-cox-proportional-hazards-model/2023-08-30T06:15:52.000ZThis post will briefly share the derivation, estimation, assumption and application of the Cox proportional hazards (PH) model. In addition, it will also mention using ANOVA to test two nested models.
]]>This post will briefly share the derivation, estimation, assumption and application of the Cox proportional hazards (PH) model. In addition, it will also mention using ANOVA to test two nested models.
]]><![CDATA[Survival Analysis 3: Non-Parametric Comparison of Survival Functions]]>https://BolinWu-Gridea.github.io/post/survival-analysis-3-non-parametric-comparison-of-survival-functions/2023-07-27T08:34:25.000ZThis post is to share the two common non-parametric tests of comparing the survival functions: Log-Rank Test & Generalized Wilcoxon Test, as well as their corresponding calculations in the detailed process.
]]>This post is to share the two common non-parametric tests of comparing the survival functions: Log-Rank Test & Generalized Wilcoxon Test, as well as their corresponding calculations in the detailed process.
]]><![CDATA[Survival Analysis 2: Non-Parametric Estimation of Survival Functions]]>https://BolinWu-Gridea.github.io/post/survival-analysis-2-non-parametric-estimation-of-survival-functions/2023-07-21T00:10:07.000ZConcepts of survival function estimations and corresponding calculations both manually and in R.
]]>Concepts of survival function estimations and corresponding calculations both manually and in R.
]]><![CDATA[Survival Analysis 1: Basic Concepts and Three Fundamental Functions]]>https://BolinWu-Gridea.github.io/post/survivalanalysis-1/2023-05-28T07:05:32.000ZThis post covers their concepts and relationship among the three pillows of survival analysis: survivor function, density function, hazard function.
]]>This post covers their concepts and relationship among the three pillows of survival analysis: survivor function, density function, hazard function.
]]><![CDATA[Network Influence Measures ]]>https://BolinWu-Gridea.github.io/post/network-influence-measures/2021-08-18T12:24:21.000ZCloseness centrality can tell us how to find important nodes in a network. The important nodes could disseminate information to many nodes or prevent epidemics, or hubs in a transportaion network, etc.
]]>Closeness centrality can tell us how to find important nodes in a network. The important nodes could disseminate information to many nodes or prevent epidemics, or hubs in a transportaion network, etc.
]]><![CDATA[Network Connectivity]]>https://BolinWu-Gridea.github.io/post/network-connectivity/2021-08-11T13:54:08.000ZIn this post I will briefly share the connectivity related concepts and functions of clustering coefficient, distance measures, and connection robustness.
]]>In this post I will briefly share the connectivity related concepts and functions of clustering coefficient, distance measures, and connection robustness.
]]><![CDATA[Network Analysis Basics]]>https://BolinWu-Gridea.github.io/post/network-analysis-basics/2021-08-04T02:04:44.000ZNetworks is a set of objects (nodes) with interconnections (edges). Many complex structures can be represented by networks. It is everywhere in different forms. For example, family network, Facebook communication network, subway network, food web, etc.
]]>Networks is a set of objects (nodes) with interconnections (edges). Many complex structures can be represented by networks. It is everywhere in different forms. For example, family network, Facebook communication network, subway network, food web, etc.
]]><![CDATA[NLP 4: Semantic Text Similarity and Topic Modeling ]]>https://BolinWu-Gridea.github.io/post/semantic-text-similarity-and-topic-modeling/2021-07-26T23:47:50.000ZTopic modeling is a useful tool for people to grasp a general picture of a long text document. Compared with LSTM or RNN, topic model is more or less for observatory purpose rather than prediction. In this post I will share the measure of similarity among words, the concept of topic modeling and its application in Python.
]]>Topic modeling is a useful tool for people to grasp a general picture of a long text document. Compared with LSTM or RNN, topic model is more or less for observatory purpose rather than prediction. In this post I will share the measure of similarity among words, the concept of topic modeling and its application in Python.
]]><![CDATA[NLP 3: Text Classification in Python]]>https://BolinWu-Gridea.github.io/post/text-classification-in-python/2021-07-22T04:15:47.000ZIn the previous two posts, I have shared basic concepts and useful functions of text mining and NLP. In this third post of text mining in Python, we finally proceed to the advanced part of text mining, that is, to build text classification model. In this post I will share the main tasks of text classification. Two useful classification models, their implementation in Python and methods of improving classification performance.
]]>In the previous two posts, I have shared basic concepts and useful functions of text mining and NLP. In this third post of text mining in Python, we finally proceed to the advanced part of text mining, that is, to build text classification model. In this post I will share the main tasks of text classification. Two useful classification models, their implementation in Python and methods of improving classification performance.
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