相关实验视频

Updated: Jul 2, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

介绍贝叶斯空间平滑方法用于疾病映射:模拟县枪支自杀死亡率和死亡率

Emma L Gause, Austin E Schumacher, Alice M Ellyson

    American journal of epidemiology
    |February 20, 2024
    PubMed
    概括

    贝叶斯空间平滑模型通过稳定不稳定的县级死亡率来改善枪支自杀地图. 这些先进的统计方法为公共卫生专业人员提供可靠的估计,即使死亡人数很少.

    关键词:
    贝叶斯空间平滑法是贝叶斯空间平滑法.疾病绘制地图.枪支自杀 枪支自杀 枪支自杀 枪支自杀小面积估计小面积估计空间统计的空间统计.

    更多相关视频

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.5K
    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.4K

    相关概念视频

    Statistical Methods for Analyzing Epidemiological Data01:25

    Statistical Methods for Analyzing Epidemiological Data

    Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
    366
    Introduction To Survival Analysis01:18

    Introduction To Survival Analysis

    Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
    The primary goal of survival analysis is to estimate survival time—the time...
    236
    Parametric Survival Analysis: Weibull and Exponential Methods01:14

    Parametric Survival Analysis: Weibull and Exponential Methods

    Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
    Weibull Distribution
    The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
    430
    Assumptions of Survival Analysis01:15

    Assumptions of Survival Analysis

    Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
    127
    Estimating Population Standard Deviation01:26

    Estimating Population Standard Deviation

    When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
    3.0K
    Estimating Population Mean with Unknown Standard Deviation01:22

    Estimating Population Mean with Unknown Standard Deviation

    In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
    William S. Gosset (1876–1937) of the...
    7.7K