Employing the one-step stochastic frontier analysis (SFA) approach, this paper examines bank efficiency in China, paying special attention to the ownership, selection effect and dynamic effects of governance changes on bank performance. Bank efficiency has improved over the data period 1995-2008. The estimated average cost and profit efficiencies are 74% and 63% respectively. Joint Stock Commer...

This study examines the cost and profit efficiency of banking sectors in twelve transition economies of Central and Eastern Europe (CEE) over the period 1993-2000, using the stochastic frontier approach (SFA) and the distribution free approach (DFA). The managerial inefficiencies in CEE banking markets were found to be significant, with average cost efficiency level for 12 countries of 72 and 7...

a College of Business Administration, Ewha Womans University, 11-1 Daehyun-dong, Seodaemun-gu, Seoul 120-750, Republic of Korea b Graduate School of Business, Sogang University, #1, Shinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea c Center for Sales Innovation, St. Catherine University, St. Paul, MN, USA d Purdue University, West Lafayette, IN, USA e Department of Marketing, College of Bu...

This study investigated various factors explaining the technical, allocative and cost efficiency of Finnish hospitals using parametric frontier models and nonparametric DEA models. DEA scores were correlated to the same set of variables as efficiency scores by stochastic frontier models. Specialization, specialization in expensive DRGs, sufficiently high proportion of resident physicians and in...

This paper presents an approach for increasing the reactivity of a humanoid robot’s gait, incorporating Slow Feature Analysis (SFA), an unsupervised learning algorithm issuing from the domain of theoretical biology. The main objective of this work is to find a means to detect disturbances in the gait pattern at an early stage without losing stability. Another goal is to investigate the general ...

Temporal slowness is a learning principle that allows learning of invariant representations by extracting slowly varying features from quickly varying input signals. Slow feature analysis (SFA) is an efficient algorithm based on this principle and has been applied to the learning of translation, scale, and other invariances in a simple model of the visual system. Here, a theoretical analysis of...

This paper proposes the event detection system for TRECVid 2011 surveillance event detection. ”CellToEar”, ”Embrace”, ”ObjectPut”, ”PeopleMeet”, ”PeopleSplitUp”, ”PersonRuns” and ”Pointing” are the 7 events we detect in our system. Firstly, interest points are detected in the Local spatial and temporal regions, and local feature is described with SFA (slow feature analysis) method. We apply lib...

Slow feature analysis (SFA) is a bioinspired method for extracting slowly varying driving forces from quickly varying nonstationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g. the envelope of a modulated sine wave). It depends on circumstances like the embedding dimension, the time series predictability, or...

This paper conducts a comparative technical efficiency analysis of 21 electricity generation plants (12 private and 9 public) using panel data of 6 years (1998-2003), and two stat-of–the art methodologies: Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). The results show a mixed technical and scale efficiency scores for the public and private generation plants. There are ...

This paper takes issues with the appropriateness of applying the stochastic frontier analysis (SFA) technique to account for environmental effects and statistical noise in the popular three-stage data envelopment analysis (DEA). A correctly specified SFA model with a censored dependent variable and the associated maximum likelihood estimation (MLE) are proposed. The simulations show that the fi...