Interval Estimation for Messy Observational Data

Large-N Observational Data Analysis (aka Messy Data): A Modest Defense

Non-Bayesian Estimation and Prediction under Weibull Interval Censored Data

In this paper, a one-sample point predictor of the random variable X is studied. X is the occurrence of an event in any successive visits $L_i$ and $R_i$ :i=1,2…,n (interval censoring). Our proposed method is based on finding the expected value of the conditional distribution of X given $L_i$ and $R_i$ (i=1,2…,n). To make the desired prediction, our approach is on the basis of approximating the...

Messy Solutions For Messy Problems

We spend|and waste|too much time discussing the pros and cons of diierent programming paradigms and too little time discussing the problems we want to solve. The rst section of this note introduces a problem; the remaining sections discuss approaches to solving it. 1 A problem The problem is to design a computer program that plays good snooker. (You can substitute \pool" or \billiards" for snoo...

channel estimation for mimo-ofdm systems

تخمین دقیق مشخصات کانال در سیستم های مخابراتی یک امر مهم محسوب می گردد. این امر به ویژه در کانال های بیسیم با ‏خاصیت فرکانس گزینی و زمان گزینی شدید، چالش بزرگی است. مقالات متعدد پر از روش های مبتکرانه ای برای طراحی و آنالیز ‏الگوریتم های تخمین کانال است که بیشتر آنها از روش های خاصی استفاده می کنند که یا دارای عملکرد خوب با پیچیدگی ‏محاسباتی بالا هستند و یا با عملکرد نه چندان خوب پیچیدگی پایینی...

Sieve Estimation with Bivariate Interval Censored Data

Bivariate interval censored data arises in many applications. However, both theoretical and computational investigations for this type of data are limited because of the complicated structure of bivariate censoring. In this paper, we propose a two-stage spline-based sieve estimator for the association between two event times with bivariate case 2 interval censored data. A smooth and explicit es...