Journal of Biometrics & Biostatistics

Nonparametric regression techniques including kernel smoothing [1], spline smoothing [2], and local regression [3] are useful for estimating a mean response function µ(x) in the statistical model Yi = µ (xi)+?i when one is unwilling to assume that µ(x) is linear (or polynomial of higher but known degree) in the covariate x. These same techniques can also be employed to estimate one or more derivatives of µ(x). While the techniques differ in their details, they have a common underlying theme. One specifies a covariate value x0 and estimates µ(x) or one of its derivatives at x0 by solving an optimization problem that is localized to a neighborhood of x0, in that only observations with covariate values inside the neighborhood contribute substantively to the solution. For example, the simplest incarnation of this theme is to define µ(x0) to be the average of all responses Yi for which |xi-x0| is sufficiently small. As one slides x0 through a continuum of all possible covariate values, an estimated mean response or derivative is then traced out. Selecting the neighborhood size is a crucial implementation decision to which much literature has been devoted.

Fingerprint is the cheapest, fastest, most convenient and most reliable way to identify someone. And the tendency, due to scale, easiness and the existing foundation, is that the use of fingerprint will only increase. Cars, cell phones, PDAs, personal computers and dozens of products and devices are using fingerprints more and more. When it comes to deal with large-scale fingerprint database, the scalability of current fingerprint recognition system is not proved yet. Our target application is a centralized user authentication system for e-commerce including online shopping malls and for user identification for government web services. In this paper, we introduce a large-scale fingerprint recognition system which incorporates fingerprint classification, large-scale identification, multiple server based identification. Our experimental result shows that our multi-server system takes 5.2 - 5.36 seconds to identify a fingerprint from 100,000 fingerprints.

When there is a rare disease in a population, it is inefficient to take a random sample to estimate a parameter. Instead one takes a random sample of all nuclear families with the disease by ascertaining at least one sibling (proband) of each family. In these studies, if the ascertainment bias is ignored, an estimate of the proportion of siblings with the disease will be inflated. The problem arises in population genetics, and it is analogous to the well-known selection bias problem in survey sampling. For example, studies of the issue of whether a rare disease shows an autosomal recessive pattern of inheritance, where the Mendelian segregation ratios are of interest, have been investigated for several decades and corrections have been made for the ascertainment bias using maximum likelihood estimation. Here, we develop a Bayesian analysis to estimate the segregation ratio in nuclear families when there is an ascertainment bias. We consider the situation in which the proband probabilities are allowed to vary with the number of affected siblings, and we investigate the effect of familial correlation among siblings within the same family. We discuss an example on cystic fibrosis and a simulation study to assess the effect of the familial correlation.

We propose a model to describe the spread of a disease among individuals regarded as fixed. The approach relies on a survival analysis technique working out times to infection. We reformulate the force of infection and introduce an infection factor referring to proportional hazard models. Properties of the MLE of the model parameters are studied. Results on real data are displayed and a simulation study is conducted.