United States trade and possible restrictions in high-technology products

This paper assesses the changing U.S. competitive position in high-technology products, and examines reasons for changes. It then inquires into the potential value of restrictive trade policies for promoting the U.S. interest in this sector, including mutual restrictions between the U.S., the EC, and Japan. The paper shows that the U.S. market share in most high-technology products, while still high, is declining. While industrial policy in other countries may have been a facilitating factor in this adverse development, the main explanation appears to lie in changing factor endowments, particularly the marked increase in the human capital/labor endowment ratios of Japan and Germany relative to that of the United States. When considering all the criteria relevant to trade policy, the differences between the high-technology industries bring into question the validity of lumping them into one sector for the purpose of strategic trade policy. Each industry needs to be treated separately. Their main common characteristic is intensity in human capital input. When they are viewed as one sector, a move to redress the declining U.S. lead calls for a domestic rather than a trade policy: massive U.S. investment in human capital and in research and development of the post-sputnik variety.     

Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB,ApoA1 and the ratio were determined using a Bayesian approach

We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification.