Longevity studies based on kernel hazard estimation
نویسندگان
چکیده
منابع مشابه
Bandwidth selection in marker dependent kernel hazard estimation
Practical estimation procedures for local linear estimation of an unrestricted failure rate when more information is available than just time are developed. This extra information could be a covariate and this covariate could be a time series. Time dependent covariates are sometimes called markers, and failure rates are sometimes called hazards, intensities or mortalities. It is shown through s...
متن کاملKernel-Based Cardinality Estimation on Metric Data
The efficient management of metric data is extremely important in many challenging applications as they occur e.g. in the life sciences. Here, data typically cannot be represented in a vector space. Instead, a distance function only allows comparing individual elements with each other to support distance queries. As high-dimensional data suffers strongly from the curse of dimensionality, distan...
متن کاملEdge Detection based on Kernel Density Estimation
Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the prob...
متن کاملa non-parametric method for hazard rate estimation in acute myocardial infarction patients: kernel smoothing approach
background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity. in this article, four indices of epanechnikov, b...
متن کاملDENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginnin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Insurance: Mathematics and Economics
سال: 2001
ISSN: 0167-6687
DOI: 10.1016/s0167-6687(00)00076-7