Advertising in the U.S. Non-Alcoholic Beverage Industry: Are Spillover Effects Negative or Positive? Revisited using a Dynamic Approach

Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, D11, D12,

Ascertaining the Impact of the 2000 USDA Dietary Guidelines for Americans on the Intake of Calories, Caffeine, Calcium, and Vitamin C from At-Home Consumption of Nonalcoholic Beverages

Obesity is one of the most pressing and widely emphasized health problems in America today. Beverage choices made by households have impacts on determining the intake of calories, calcium, caffeine, and vitamin C. Using data from the Nielsen Homescan Panel over the period 1998–2003, and a two-way random-effects Fuller-Battese error components procedure, we estimate econometric models to examine economic and demographic factors affecting per-capita daily intake of calories, calcium, caffeine, and vitamin C derived from the consumption of nonalcoholic beverages. Our study demonstrates the effectiveness of the USDA 2000 Dietary Guidelines in reducing caloric and nutrient intake associated with nonalcoholic beverages.Nielsen Homescan Panel, nonalcoholic beverages, nutrient and caloric intake, USDA Dietary Guidelines, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, D10, D12, I10, I18,

Nutritional Contributions of Nonalcoholic Beverages to the U.S. Diet: 1998-2003

Using data from U.S. households over the period 1998 to 2003, we examine economic and demographic factors affecting per capita daily intake of calories, calcium, caffeine, and vitamin C derived from the consumption of nonalcoholic beverages. Our study demonstrates the effectiveness of the USDA 2000 Dietary Guidelines in reducing such caloric and nutrient intake.nonalcoholic beverages, nutritional elements, calories, calcium, vitamin C, caffeine, and econometric analysis, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Application of sequential nonparametric confidence bands in finance

In a nonparametric setting, the functional form of the relationship between the response variable and the associated predictor variables is assumed to be unknown when data is fitted to the model. Non-parametric regression models can be used for the same types of applications such as estimation, prediction, calibration, and optimization that traditional regression models are used for. The main aim of nonparametric regression is to highlight an important structure in the data without any assumptions about the shape of an underlying regression function. Hence the nonparametric approach allows the data to speak for itself. Applications of sequential procedures to a nonparametric regression model at a given point are considered. The primary goal of sequential analysis is to achieve a given accuracy by using the smallest possible sample sizes. These sequential procedures allow an experimenter to make decisions based on the smallest number of observations without compromising accuracy. In the nonparametric regression model with a random design based on independent and identically distributed pairs of observations (X ,Y ), where the regression function m(x) is given bym(x) = E(Y X = x), estimation of the Nadaraya-Watson kernel estimator (m (x)) NW and local linear kernel estimator (m (x)) LL for the curve m(x) is considered. In order to obtain asymptotic confidence intervals form(x), two stage sequential procedure is used under which some asymptotic properties of Nadaraya-Watson and local linear estimators have been obtained. The proposed methodology is first tested with the help of simulated data from linear and nonlinear functions. Encouraged by the preliminary findings from simulation results, the proposed method is applied to estimate the nonparametric regression curve of CAPM.<br /

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1&minus;. Here, d(&gt; 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.<br /

Teaching Data Analysis with Interactive Visual Narratives

Data analysis is a major part of business analytics (BA), which refers to the skills, methods, and technologies that enable managers to make swift, quality decisions based on large amounts of data. BA has become a major component of Information Systems (IS) courses all over the world. The challenge for IS educators is to teach data analysis – the foundational BA concepts – to early years undergraduate students who commonly have an aversion to statistics as well as poor problem-solving skills. This article describes the development and evaluation of a learning intervention, Interactive Visual Narratives (IVN), which is informed by previous research into the efficacy of interaction, visualization, and narratives across a variety of learning contexts. The results suggest that a combination of interactive visualizations and narratives can improve the acquisition of data analysis knowledge, facilitate essential skills in problem analysis and the application of BA solutions, and enhance student engagement. These findings provide useful insights for improving students’ learning outcomes and engagement

Statistical comparisons of non-deterministic IR systems using two dimensional variance

Retrieval systems with non-deterministic output are widely used in information retrieval. Common examples include sampling, approximation algorithms, or interactive user input. The effectiveness of such systems differs not just for different topics, but also for different instances of the system. The inherent variance presents a dilemma - What is the best way to measure the effectiveness of a non-deterministic IR system? Existing approaches to IR evaluation do not consider this problem, or the potential impact on statistical significance. In this paper, we explore how such variance can affect system comparisons, and propose an evaluation framework and methodologies capable of doing this comparison. Using the context of distributed information retrieval as a case study for our investigation, we show that the approaches provide a consistent and reliable methodology to compare the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non-deterministic IR system has equivalent effectiveness to another IR system, and how to avoid the common pitfall of misusing a lack of significance as a proof that two systems have equivalent effectiveness

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1&minus;. Here, d(&gt; 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.<br /

The RNA binding protein HuR does not interact directly with HIV-1 reverse transcriptase and does not affect reverse transcription in vitro

<p>Abstract</p> <p>Background</p> <p>Lemay <it>et al </it>recently reported that the RNA binding protein HuR directly interacts with the ribonuclease H (RNase H) domain of HIV-1 reverse transcriptase (RT) and influences the efficiency of viral reverse transcription (Lemay <it>et al</it>., 2008, Retrovirology 5:47). HuR is a member of the embryonic lethal abnormal vision protein family and contains 3 RNA recognition motifs (RRMs) that bind AU-rich elements (AREs). To define the structural determinants of the HuR-RT interaction and to elucidate the mechanism(s) by which HuR influences HIV-1 reverse transcription activity <it>in vitro</it>, we cloned and purified full-length HuR as well as three additional protein constructs that contained the N-terminal and internal RRMs, the internal and C-terminal RRMs, or the C-terminal RRM only.</p> <p>Results</p> <p>All four HuR proteins were purified and characterized by biophysical methods. They are well structured and exist as monomers in solution. No direct protein-protein interaction between HuR and HIV-1 RT was detected using NMR titrations with ¹⁵N labeled HuR variants or the ¹⁵N labeled RNase H domain of HIV-1 RT. Furthermore, HuR did not significantly affect the kinetics of HIV-1 reverse transcription <it>in vitro</it>, even on RNA templates that contain AREs.</p> <p>Conclusions</p> <p>Our results suggest that HuR does not impact HIV-1 replication through a direct protein-protein interaction with the viral RT.</p