Strategic Product/Service Innovations of an Online Firm

We study the incentives that drive an online firm to make various types of innovations in a competitive environment. We develop and use a simplified price competition model between two retailers, one online and one offline. A given fraction of consumers, called the Internet penetration, comparison shop online, independent of their customer type, thereby creating two markets for the offline retailer, a captive market and a competitive market. The online product has the steeper of the two linear utility functions, which means that the customers who buy online in our model are high end. We focus on the competitive region in which both retailers are (strictly) profitable in the competitive market and consider innovations that increase high‐end appeal, low‐end appeal, and/or reduce unit cost. We find that the online firm has a strong incentive to invest in innovations that either reduce unit cost and/or, equivalently, increase the appeal to all consumers equally. Investments of this type are strategic complements: implementing one increases the value of another, so the value of two innovations of this type is more than the sum of the values of each individually. We identify a relative strength measure of the online firm such that, as its high‐end appeal increases and/or its unit cost decreases, we say that the online firm is stronger. This strength measure facilitates drawing an explicit dividing line between strong and weak online firms. If Internet penetration increases, the online firm's profits increase if and only if it is strong. If penetration increases over time, it is possible for a strong firm to turn weak and see its profits decrease and possibly disappear completely. A strong online firm has more opportunity to profit from low‐end innovations than does a weak one, while the opposite is true for high‐end innovations. Interestingly, some innovations may actually decrease the online firm's profits. We discuss the implications of our results for existing and future online innovations.     

Slack free MEA and RDM with comprehensive efficiency measures

This paper extends two directional distance function models, the Multi-directional Efficiency Analysis (MEA) Model and the Range Directional Model (RDM), in order to account for any type of technical inefficiency, i.e. both directional and non-directional inefficiencies. We first focus on the variable returns to scale (VRS) case, because both VRS-MEA and RDM are translation invariant models, which mean that both models are able to deal with negative data. Our main result is the definition of a new comprehensive efficiency measure which is units invariant and translation invariant and covers both models. Secondly, we introduce the RDM model under constant returns to scale (CRS) together with a new comprehensive efficiency measure.     

Hepatitis C psychoeducation curriculum topics generated by group members

ABSTRACT

Educational programs for hepatitis C (HCV) have been created by professionals. There has not been a focus on whether what clients want to know about HCV differs from what providers want patients to know. This study addresses this gap by examining topics selected by participants in an education/support group model. Data were collected as part of a randomized psychoeducation trial for patients with HCV. Topics were systematically generated by the members uniquely for each group. Topics and rank order for each group were generated and a combined variable was created. Twenty-eight different topics were identified across 12 groups. The total number of topics was 102, averaging 8.5 per group. Topics are divided into three separate conceptual groups, members’ personal relationship to HCV, medical aspects of HCV, and unique concerns. A patient-centered approach that addresses the needs of HCV patients first provides an optimal starting point to discuss HCV disease management.     

Combining an Internal Pilot with an Interim Analysis for Single Degree of Freedom Tests

An internal pilot with interim analysis (IPIA) design combines interim power analysis (an internal pilot) with interim data analysis (two-stage group sequential). We provide IPIA methods for single df hypotheses within the Gaussian general linear model, including one and two group t tests. The design allows early stopping for efficacy and futility while also re-estimating sample size based on an interim variance estimate. Study planning in small samples requires the exact and computable forms reported here. The formulation gives fast and accurate calculations of power, Type I error rate, and expected sample size.     

A Nonparametric Synthetic Control Chart Using Sign Statistic

This article presents a synthetic control chart for detection of shifts in the process median. The synthetic chart is a combination of sign chart and conforming run-length chart. The performance evaluation of the proposed chart indicates that the synthetic chart has a higher power of detecting shifts in process median than the Shewhart charts based on sign statistic as well as the classical Shewhart X-bar chart for various symmetric distributions. The improvement is significant for shifts of moderate to large shifts in the median. The robustness studies of the proposed synthetic control chart against outliers indicate that the proposed synthetic control chart is robust against contamination by outliers.     

Tests for Equality of Inflation Parameters of Two Zero-Inflated Power Series Distributions

In this article, we consider two independent zero-inflated power series distributions and provide likelihood ratio test for equality of inflation parameters of the same. As an illustration, testing equality of inflation parameters of two zero inflated Poisson distributions is provided. Further, simulation study to investigate power of likelihood ratio tests has been carried out.     

On the Use of a Log-Rate Model for Survey-Weighted Categorical Data

For the analysis of survey-weighted categorical data, one recommended method of analysis is a log-rate model. For each cell in a contingency table, the survey weights are averaged across subjects and incorporated into an offset for a loglinear model. Supposedly, one can then proceed with the analysis of unweighted observed cell counts. We provide theoretical and simulation-based evidence to show that the log-rate analysis is not an effective statistical analysis method and should not be used in general. The root of the problem is in its failure to properly account for variability in the individual weights within cells of a contingency table. This results in goodness-of-fit tests that have higher-than-nominal error rates and confidence intervals for odds ratios that have lower-than-nominal coverage.     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

Nonparametric Predictive Inference for Ordinal Data

Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework based only on an exchangeability assumption for future and past observations, made possible by the use of lower and upper probabilities. In this article, NPI is presented for ordinal data, which are categorical data with an ordering of the categories. The method uses a latent variable representation of the observations and categories on the real line. Lower and upper probabilities for events involving the next observation are presented, and briefly compared to NPI for non ordered categorical data. As application, the comparison of multiple groups of ordinal data is presented.     

An Investigation of Agreement Between the Item Difficulty Coefficient Calculated in Accordance With Classical Test Theory and Item Response Theory With Bland–Altman Method

The purpose of this study is to investigate agreement between item difficulty coefficients calculated relying on classical test theory and item response theory with Bland–Altman method. According to results, although there is a high correlation between Pj and b coefficient estimated with HGLM (hierarchical generalized linear model), 1P, and 3P models, it can be said that there is no agreement between two methods and cannot be used interchangeably. It is observed that the confidence limit is wide according to Bland–Altman graphics. Therefore, it can be said that there is no agreement between item difficulty values obtained from two methods. Bland–Altman method which is used in clinical studies mostly is suggested to be used in the comparison of methods used especially in the evaluation of student performance in education, in agreement studies among specialist considerations especially in terms of providing additional information to the studies in which correlation coefficient is calculated.