Third-Degree Price Discrimination, Consumption Externalities, and Market Opening

This study investigates the effects of monopolistic third-degree price discrimination on market opening in the presence of consumption externalities between separate markets. Assuming symmetric interdependent linear demands and constant marginal cost, we indicate the possibility that with negative externalities a monopolist can do better by closing the relatively small market from the social welfare viewpoint, while it prefers opening that market if price discrimination is feasible. This result contradicts the previous literature on third-degree price discrimination and market opening which asserts that, in the case of non-increasing marginal cost, price discrimination improves social welfare if it opens new markets that are closed under uniform pricing.     

Hepatocellular carcinoma: Prospective assessments of the T-factor with CT,US, and MR imaging

The capabilities of computed tomography (CT), ultrasonography (US), and magnetic resonance (MR) imaging were studied in order to determine the role of each of these noninvasive examinations for estimating the T-factor of hepatocellular carcinomas (HCCs). Fifty-one patients with surgically proven HCCs received CT (50 patients), US (46 patients), and MR (44 patients). The images of CT, US, and MR were prospectively evaluated for main tumor size, intrahepatic metastases, and vascular invasion, which compose the T-factor of HCC, and compared to pathological results. The sizes of the main tumor were estimated correctly by all examinations. For estimating intrahepatic metastases, US (74%) and MR (73%) were superior to CT (65%). For estimating portal invasion, CT (79%) was superior to US (70%) and MR (66%), because CT could demonstrate the segmental staining caused by portal invasion. The estimates of hepatic venous invasion were difficult during any of the examinations. We conclude that presurgical evaluations of the T-factor require the use of US and CT or MR and CT.     

The Japanese financial system in comparative perspective

Major elements of financial market structure in Japan and the U. S. are compared for the postwar period. It is found that the ratio of financial liabilities of the nonfinancial sector to GNP rose continuously in Japan, but was stable in the U. S. Intermediation was deeper in Japan than in the U. S., and the Japanese government's chief role was as an intermediary rather than as borrower or regulator. There was more competition in deposit collection in Japan than in the U. S. It is hypothesized that the depth and competition in Japanese financial structure helped raise savings, investment, and GNP growth.     

Estimating noncooperative and cooperative models of bargaining: an empirical comparison

This paper examines the issue of model selection in studies of strategic situations. In particular, we compare estimation results from a noncooperative formulation of government formulation à la (Baron and Ferejohn in Am Poli Sci Rev 87:34–47, 1989) with those from two alternative cooperative formulations (Nash in Econometrica 18:155–162, 1950; Shapley and Shubik in Am Poli Sci Rev 48:787–792, 1954). Although the estimates of the ministerial ranking are similar, statistical testing suggests that the noncooperative formulation is best fitted to the observed data among the alternative models. This result implies that modeling the noncooperative structure in bargaining situations is crucially important at the risk of possibly misspecifying the details of the game.     

Cost-effectiveness of intravitreal aflibercept versus other treatments for wet age-related macular degeneration in Japan

Objective: This analysis estimated the cost-effectiveness of intravitreal aflibercept injection(s) (IAI) for wet age-related macular degeneration (wAMD) compared with other treatments in Japan.

Methods: This was a cost-utility analysis based on published data. A state-transition cohort model was constructed with six health states based on best-corrected visual acuity in the better-seeing eye. The cycle time was 4 weeks, and the time horizon was 12 years. The model compared IAI 2?mg every 8 weeks (2q8) for 2 years after three initial monthly injections, ranibizumab as needed, ranibizumab 0.5?mg every 4 weeks (0.5q4), pegaptanib sodium 0.3?mg every 6 weeks, verteporfin photodynamic therapy (PDT), and best supportive care, assumed to include medical management and monitoring, but no active therapy. Costs (expressed as Japanese yen [JPY]) and quality-adjusted life years (QALYs) gained were estimated for each treatment and discounted at 2.0%. Input data were obtained from clinical studies, the Japanese drug tariff and social insurance reimbursement schedule, and expert opinion. The analysis was conducted from the societal perspective, including medical costs as well as costs of blindness.

Results: IAI 2q8 was dominant (i.e. more effective in terms of QALYs and less costly) to all other comparators (ranibizumab as needed, ranibizumab 0.5q4, pegaptanib sodium, PDT, and best supportive care), as shown by the incremental cost-utility ratio (i.e. cost per QALY gained).

Limitations: The strengths of the analysis include the wide range of comparators evaluated and the use of Japanese-specific utility data. The limitations include the use of one eye, inclusion of published data up to 2 years only, and assumptions on disease course over 5 years.

Conclusions: IAI 2q8 was more effective in terms of QALYs and less costly compared with other treatments for wAMD in Japan.     

Personal Authentication Using a Kinect Sensor

This article proposes a new approach to personal authentication by exploring the features of a person’s face and voice. Microsoft’s Kinect sensor is used for facial and voice recognition. Parts of the face including the eyes, nose, and mouth, etc., are analyzed as position vectors. For voice recognition, a Kinect microphone array is adopted to record personal voices. Mel-frequency cepstrum coefficients, logarithmic power, and related values involved in the analysis of personal voice are also estimated from the voices. Neural networks,support vector machines and principal components analysis are employed and compared for personal authentication. To achieve accurate results, 20 examinees were selected for face and voice data used for training the authentication models. The experimental results show that the best accuracy is achieved when the model is trained by a support vector machine using both facial and voice features.