{"id":19883562,"url":"https://github.com/apachecn/statsmodels-doc-zh","last_synced_at":"2025-08-18T12:09:57.963Z","repository":{"id":52687422,"uuid":"160902035","full_name":"apachecn/statsmodels-doc-zh","owner":"apachecn","description":"Statsmodels: Python中的统计建模与计量统计学类库，此为ApacheCN推出的中文版翻译。","archived":false,"fork":false,"pushed_at":"2021-04-21T03:35:32.000Z","size":3551,"stargazers_count":172,"open_issues_count":1,"forks_count":56,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-05-29T19:16:58.880Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/apachecn.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGES.md","contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-12-08T03:58:08.000Z","updated_at":"2025-05-12T09:32:11.000Z","dependencies_parsed_at":"2022-08-22T06:21:28.351Z","dependency_job_id":null,"html_url":"https://github.com/apachecn/statsmodels-doc-zh","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/apachecn/statsmodels-doc-zh","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fstatsmodels-doc-zh","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fstatsmodels-doc-zh/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fstatsmodels-doc-zh/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fstatsmodels-doc-zh/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/apachecn","download_url":"https://codeload.github.com/apachecn/statsmodels-doc-zh/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fstatsmodels-doc-zh/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270989168,"owners_count":24680688,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-18T02:00:08.743Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T17:21:14.548Z","updated_at":"2025-08-18T12:09:57.936Z","avatar_url":"https://github.com/apachecn.png","language":null,"readme":"## 这是什么？\n\n这是由ApacheCN翻译的Statsmodels的中文文档\n\n## Statsmodels是什么？\n\nStatsmodels是一个Python包，为统计计算的scipy提供补充，包括描述性统计和统计模型的估计和推断。\n\n### 英文文档\n\n最新的稳定版文档位于\n\n[https://www.statsmodels.org/stable/](https://www.statsmodels.org/stable/)\n\n开发版文档位于\n\n[https://www.statsmodels.org/dev/](https://www.statsmodels.org/dev/)\n\nRecent improvements are highlighted in the release notes\n\n[https://www.statsmodels.org/stable/release/version0.9.html](https://www.statsmodels.org/stable/release/version0.9.html)\n\n有关文档的备份请访问-[https://statsmodels.github.io/stable/](https://statsmodels.github.io/stable/)\n和 [https://statsmodels.github.io/dev/](https://statsmodels.github.io/dev/).\n\n### 主要内容\n\n - 线性回归模型：\n    - 普通最小二乘\n    - 广义最小二乘法\n    - 加权最小二乘法\n    - 具有自回归误差的最小二乘法\n    - 分位数回归\n    - 递归最小二乘法\n - 具有混合效应和方差分量的混合线性模型\n - GLM：支持所有单参数指数族分布的广义线性模型\n - 用于二项式和泊松的贝叶斯混合GLM\n - GEE：单向聚类或纵向数据的广义估计方程\n - 离散模型：\n    - Logit和Probit\n    - 多项logit（MNLogit）\n    - 泊松和广义Poisson回归\n    - 负二项式回归\n    - 零膨胀计数模型\n - RLM：强大的线性模型，支持多个M估计器。\n - 时间序列分析：时间序列分析模型\n    - 完成StateSpace建模框架\n        - 季节性ARIMA和ARIMAX模型\n        - VARMA和VARMAX型号\n        - 动态因子模型\n        - 未观察到的组件模型\n    - 马尔可夫切换模型（MSAR），也称为隐马尔可夫模型（HMM）\n    - 单变量时间序列分析：AR，ARIMA\n    - 矢量自回归模型，VAR和结构VAR\n    - 矢量误差修正模型，VECM\n    - 指数平滑，Holt-Winters\n    - 时间序列的假设检验：单位根，协整等\n    - 时间序列分析的描述性统计和过程模型\n - 生存分析：\n    - 比例风险回归（Cox模型）\n    - 幸存者函数估计（Kaplan-Meier）\n    - 累积发生率函数估计\n - 多因素：\n    - 缺少数据的主成分分析\n    - 旋转因子分析\n    - MANOVA\n    - 典型相关\n - 非参数统计：单变量和多变量核密度估计\n - 数据集：用于示例和测试的数据集\n - 统计：广泛的统计测试\n    - 诊断和规范测试\n    - 拟合优度和正态性测试\n    - 用于多个测试的功能\n    - 各种额外的统计测试\n - MICE估算，秩序统计回归和高斯插补\n - 调解分析\n - 图形包括用于数据和模型结果的可视分析的绘图功能\n - I / O\n    - 用于读取Stata .dta文件的工具，但是pandas具有更新版本\n    - 表输出为ascii，latex和html\n - Miscellaneous models(各种各样的模型）\n - Sandbox：statsmodels包含一个沙箱文件夹，其中的代码处于开发和测试的各个阶段，不被视为“生产就绪”。这包括其中之一\n    - 广义矩量法（GMM）估计量\n    - 核回归\n    - scipy.stats.distributions的各种扩展\n    - 面板数据模型\n    - 信息理论措施\n\n## ApacheCN是什么？\n\n* 主页：[apachecn.org](http://www.apachecn.org)\n* Github：[@ApacheCN](https://github.com/apachecn)\n* 社区：[community.apachecn.org](http://community.apachecn.org)\n* 知识库：[cwiki.apachecn.org](http://cwiki.apachecn.org/)\n* 自媒体平台：\n    * [微博：@ApacheCN](https://weibo.com/u/6326715527)\n    * [知乎：@ApacheCN](https://www.zhihu.com/people/apachecn)\n    * [CSDN](https://blog.csdn.net/wizardforcel/article/category/8437073)、[简书](https://www.jianshu.com/c/4ee721d0c474)、[OSChina](https://my.oschina.net/repine/)、[博客园](https://www.cnblogs.com/wizardforcel/category/1352397.html)\n* **我们不是 Apache 的官方组织/机构/团体，只是 Apache 技术栈（以及 AI）的爱好者！**\n* 如有侵权，请联系邮箱：【片刻】\u003cjiang-s@163.com\u003e（如果需要合作，也可以私聊我）\n\n## 参与翻译 \u0026 发现错误\n\n    1. 在 github 上 fork 该 repository.\n    2. 翻译 docs/zh/source 或者根目录 下面的 rst或txt 文件即可, 例如, gettingstarted.rst.\n    3. 然后, 在你的 github 发起 New pull request 请求.\n\n## 角色分配\n目前有如下可分配的角色:\n\n* 翻译: 负责文章内容的翻译.\n* 校验: 负责文章内容的校验, 比如格式, 正确度之类的.\n* 负责人: 负责整个 Projcet\n\n有兴趣参与的朋友, 可以看看最后的联系方式.\n\n## Statsmodels负责人\n* [@FontTian](https://github.com/FontTian)（Font Tian）\n\n## 贡献者\n\n贡献者可自行编辑如下内容（排名不分先后）.\n\n### Statsmodels-0.9\n**翻译者(人人皆大佬~):**\n\n* [@FontTian](https://github.com/FontTian)（Font Tian）\n\n\n## 联系方式\n有任何建议反馈, 或想参与文档翻译, 麻烦联系下面的企鹅:\n\n* 企鹅: 2404846224((FontTian)\n\n## 其它中文文档\n\n1. [sklearn 中文文档](https://github.com/apachecn/scikit-learn-doc-zh)\n2. [pytorch 0.3 中文文档](https://github.com/apachecn/pytorch-doc-zh)\n3. [TensorFlow R1.2 中文文档](http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030122)\n4. [xgboost 中文文档](https://github.com/apachecn/xgboost-doc-zh)\n5. [lightgbm 中文文档](https://github.com/apachecn/lightgbm-doc-zh)\n6. [fasttext 中文文档](https://github.com/apachecn/fasttext-doc-zh)\n7. [gensim 中文文档](https://github.com/apachecn/gensim-doc-zh)\n1. [Spark 中文文档](https://github.com/apachecn/spark-doc-zh)\n2. [Storm 中文文档](https://github.com/apachecn/storm-doc-zh)\n3. [Kafka 中文文档](https://github.com/apachecn/kafka-doc-zh)\n4. [Flink 中文文档](https://github.com/apachecn/flink-doc-zh)\n5. [Beam 中文文档](https://github.com/apachecn/beam-site-zh)\n6. [Zeppelin 0.7.2 中文文档](https://github.com/apachecn/zeppelin-doc-zh)\n7. [Elasticsearch 5.4 中文文档](https://github.com/apachecn/elasticsearch-doc-zh)\n8. [Kibana 5.2 中文文档](https://github.com/apachecn/kibana-doc-zh)\n9. [Kudu 1.4.0 中文文档](https://github.com/apachecn/kudu-doc-zh) \n0. [Spring Boot 1.5.2 中文文档](https://github.com/apachecn/spring-boot-doc-zh)\n1. [Airflow 中文文档](https://github.com/apachecn/airflow-doc-zh)\n1. [Solidity 中文文档](https://github.com/apachecn/solidity-doc-zh)\n1. [numpy 中文文档](https://github.com/apachecn/numpy-ref-zh)\n1. [pandas 中文文档](https://github.com/apachecn/pandas-doc-zh)\n1. [matplotlib 中文文档](https://github.com/apachecn/matplotlib-user-guide-zh)\n1. 等等","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapachecn%2Fstatsmodels-doc-zh","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapachecn%2Fstatsmodels-doc-zh","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapachecn%2Fstatsmodels-doc-zh/lists"}