This paper presents a new classifier combination technique based on the DempsterShafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to t...

The modeling of relationships between motor vehicle crashes and underlying factors has been investigated for more than three decades. Recently, many highway safety studies have documented the use of negative binomial (NB) regression models. On rare occasions, the Poisson model may be the only alternative especially when crash sample mean is low. Pearson's X(2) and the scaled deviance (G(2)) are...

of the United States 2009, p. 178. 10.49 According to the Federal Reserve Board, the mean net worth of U.S. households headed by persons 75 years or older is $640,000. Suppose a simple random sample of 50 households in this age group is obtained from a certain E X E R C I S E S 5217X_10_ch10_p311-363.indd 336 04/02/10 8:40 PM Chapter 10: Hypothesis Tests Involving a Sample Mean or Proportion 33...

in this paper, economic convergence in selected oic (36 countries) is studied. the convergence hypothesis is tested through three approaches: beta convergence, sigma convergence, and time series model. ols is used for estimating the cross section model and cross variance is used to test the distributive model. for analyzing time series model, we used augmented dicky fuller test. beta convergenc...

One of the simplest and most widely used multiple comparison tests is the Bonferroni adjustment (test). It is applicable to the following situations: 1) Regardless of whether or not the sample sizes of each group are equal. 2) When the family of interest is the particular set of comparisons (not all pair-wise comparisons). We can easily obtain the results of the Bonferroni comparison by multipl...

In this manuscript, we briefly introduce several tricks to climb the leaderboards which use RMSE for evaluation without exploiting any training data.

increases. Taking this characteristic into consideration, we could confront the following circumstances: when the sample size is very small, we obtain a P-value of the normality test greater than 0.05 in most cases, so we conclude that the data are from a normally distributed population even though it apparently looks like a non-normal distribution. And when the sample size is very large, we fr...