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The teaching-learning model that still predominates in higher education is the traditional one, based on the master class taught by the teacher. However, it is necessary to resort to other models of teaching and learning that are more flexible and active for students. For this reason, the main objective of this paper is to apply the flipped classroom teaching-learning model in order to test and compare if the academic results of students with the flipped classroom model improve with respect to the traditional model at different educational levels. In addition, the students' assessment of this teaching-learning model and the use of Information and Communication Technologies is analyzed. This study presents the experience of teaching innovation based on the inverted class model, which has been carried out with 155 students belonging to different educational levels and with six subjects related to the Financial Economics discipline. For this, a descriptive statistical analysis is performed, as well as analysis of contingency tables to detect the degree of association between the educational level and the teaching-learning model applied for the evaluation of the students. Evidence is obtained that university students obtain better academic results with the traditional model, while non-university students achieve better grades with the flipped classroom model. However, the students' assessment of the flipped classroom model has been very positive, regardless of educational level. The results suggest that the flipped classroom model improves the academic results of non-university students because they are more accustomed to the application of new technologies than university students. El modelo de enseñanza-aprendizaje que todavía predomina en la educación superior es el modelo tradicional basado en la clase magistral por parte del profesor. Sin embargo, es necesario recurrir a otros modelos de enseñanza y aprendizaje que sean más flexibles y activos para los estudiantes. Por este motivo, el principal objetivo de este artículo es aplicar el modelo de enseñanza-aprendizaje basado en la clase invertida para contrastar y comparar si mejora los resultados académicos de los estudiantes con respecto al modelo tradicional en diferentes niveles educativos. Además, también se analiza la valoración de los estudiantes sobre este modelo activo de enseñanza-aprendizaje y sobre el uso de las Nuevas Tecnologías de la Información y de la Comunicación. Este estudio presenta la experiencia de innovación docente basada en el modelo de clase invertida, que ha sido llevada a cabo con 155 estudiantes pertenecientes a diferentes niveles educativos y con seis asignaturas relacionadas con la disciplina Economía Financiera. Para ello, se realizan análisis estadísticos descriptivos y análisis de tablas de contingencia para detectar el grado de asociación entre el nivel educativo y la metodología de enseñanza-aprendizaje aplicada para la evaluación de los estudiantes, así como la valoración de los estudiantes sobre esta metodología activa de enseñanza-aprendizaje. Se obtiene evidencia de que los estudiantes universitarios consiguen mejores resultados académicos con la metodología tradicional mientras que los no universitarios logran mejores calificaciones con la metodología basada en la clase invertida. Sin embargo, la valoración de los estudiantes sobre la metodología basada en la clase invertida ha sido muy positiva, independientemente del nivel educativo al que pertenecen. Los resultados sugieren que el modelo basado en la clase invertida mejora los resultados académicos de los estudiantes no universitarios porque ellos están más acostumbrados a la aplicación de nuevas tecnologías que los alumnos universitarios.

Challenge: The "flipped classroom" is a pedagogical model in which instructional materials are delivered to learners outside of class, reserving class time for application of new principles with peers and instructors. Active learning has forever been an elusive ideal in medical education, but the flipped class model is relatively new to medical education. What is the evidence for the "flipped classroom," and how can these techniques be applied to the teaching of dermatology to trainees at all stages of their medical careers?

Author(s): Solomon, Marjorie; Iosif, Ana-Maria; Reinhardt, Vanessa P; Libero, Lauren E; Nordahl, Christine W; Ozonoff, Sally; Rogers, Sally J; Amaral, David G | Abstract: We examined phenotypes of autism spectrum disorder (ASD) based on trajectories of intellectual development from early (ages 2-3 ½) to middle (ages 5-8) childhood in a recent clinically ascertained cohort. Participants included 102 children (82 males) initially diagnosed with ASD from the Autism Phenome Project longitudinal sample. Latent class growth analysis was used to identify distinct IQ trajectories. Baseline and developmental course differences among groups were assessed using univariate techniques and repeated measures regression models, respectively. A four class model best represented the data. Using the highest posterior probability, participants were assigned to High Challenges (25.5%), Stable Low (17.6%), Changers (35.3%), and Lesser Challenges (21.6%) groups. The High Challenges and Stable Low groups exhibited persistently low IQ, although, the High Challenges group experienced declines while the Stable Low group's scores remained more constant. Changers showed IQ improvement of g 2 standard deviations. The Lesser Challenges group had IQs in the average range at both times that were about 1 standard deviation higher at T2. In summation, 75% of the participants experienced some relative improvements in intellectual and/or other areas of functioning between ages 2 and 8 years. The Changers group demonstrated the most significant IQ change that was accompanied by adaptive communication improvement and declining externalizing symptoms. Only the Lesser Challenges group showed a significant reduction in ASD symptom severity, such that by age 8, 14% of them no longer met ADOS-2 criteria for ASD. All groups showed reductions in internalizing symptoms. Intervention history was not associated with group status. Autism Res 2018, 11: 121-132. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.Lay summaryWe examined how the IQs of children with autism spectrum disorder change between ages 2 and 8, and identified four patterns. Two groups exhibited persistently lower IQs. One group showed IQ increases of greater than 30 points with improved communicate abilities and declining disruptive behaviors. The final group had IQs in the average or better range at both time points, and 14% of them lost their diagnoses. Over half of the children experienced improved intellectual functioning between ages 2 and 8, whereas about 25% showed declines. Findings were not associated with intervention history.

Stick-slip dynamics during nanoscratching is investigated for the Ni 62 Nb 38 metallic glass. Detrended fluctuation analysis is introduced to explore the influence of loading force on the temporal scaling and stick-slip behavior. The self-similar characteristics and complexity in the temporal scale of the lateral force signal are investigated. A modified Cauchy class model is used for the stochastic stick-slip process, which connects the fractal dimension and the Hurst exponent and features the positive correlation process. The confidence intervals of the differential friction coefficient at different loading forces elucidate the inhomogeneous (and homogeneous) shear-branching processes during the nanoscratching process.

Multiple studies have demonstrated that the information included in the Medical Student Performance Evaluation fails to reliably predict medical students’ future performance. This faulty transfer of information can lead to harm when poorly prepared students fail out of residency or, worse, are shuttled through the medical education system without an honest accounting of their performance. Such poor learner handovers likely arise from two root causes: (1) the absence of agreed-on outcomes of training and/or accepted assessments of those outcomes, and (2) the lack of standardized ways to communicate the results of those assessments. To improve the current learner handover situation, an authentic, shared mental model of competency is needed; high-quality tools to assess that competency must be developed and tested; and transparent, reliable, and safe ways to communicate this information must be created. To achieve these goals, the authors propose using a learner handover process modeled after a patient handover process. The CLASS model includes a description of the learner’s Competency attainment, a summary of the Learner’s performance, an Action list and statement of Situational awareness, and Synthesis by the receiving program. This model also includes coaching oriented towards improvement along the continuum of education and care. Just as studies have evaluated patient handover models using metrics that matter most to patients, studies must evaluate this learner handover model using metrics that matter most to providers, patients, and learners.

This paper presents a flight envelope protection system for NASA’s Transport Class Model. The developed protection scheme is based on a command-limiting architecture that accounts for aircraft adverse aerodynamics, unusual attitude, and structural integrity and is designed to augment a standard gain-scheduled flight control law. The scheme also includes an energy protection system, which relies on an automatic throttle control loop that implements a total energy control law. In the setup adopted, the limits of the protected envelopes are actively adjusted to ensure, on the one hand, that the aircraft always stays in a safe flight condition and, on the other hand, that the pilot has enough control authority to perform aggressive maneuvers, if needed. Batch simulations demonstrate the efficacy of the protection system in maintaining the aircraft within desired safe conditions.

This paper estimates and forecasts West Texas Intermediate (WTI) crude oil market volatility with GARCH, EGARCH models. We find that, in terms of Root Mean Squared Error (RMSE) criteria, EGARCH model outperformances GARCH model in both short term and long term forecast length. Then, we apply Markov Switching model on oil volatility term and find that we can improve forecast accuracy of oil market volatility further with MSGARCH model, when compared with normal GARCH and normal EGARCH model. Besides, empirical results confirm that two regimes consist of regime 1 with low volatility and regime 2 with high volatility exist in WTI crude oil market. Also, the smoothed probabilities indicate that before 2004, WTI crude oil market stays in a low volatility regime most of time, while after 2004, WTI crude oil market switches to high volatility regime and stays in it until present most of time.

The CLASS model is a top-down capital stress testing framework that uses public data, simple econometric models and auxiliary assumptions to project the effect of macroeconomic scenarios on U.S. banking firms. Through the lens of the model, we find that the total banking system capital shortfall under stressful macroeconomic conditions began to rise 4 years before the financial crisis, peaking in the fourth quarter of 2008. The capital gap has since fallen sharply, and is now significantly below pre-crisis levels. In the cross-section, banking firms estimated to be most sensitive to macroeconomic conditions also have higher capital ratios, consistent with a “precautionary” view of bank capital, though this behavior is evident only since the crisis. We interpret our results as evidence that the resiliency of the U.S. banking system has improved since the financial crisis, and also as an illustration of the value of stress testing as a macroprudential policy tool.

Sanqi is a widely used traditional Chinese medicines (TCM) for its outstanding efficacy. In Chinese market, Sanqi powder is the goal of counterfeiting for a long time. Investigation of Sanqi authenticity is very important in both economic and public health terms. The present work aims at studying the feasibility of combining near-infrared (NIR) spectroscopy with relief-based variable selection and class-modeling for identifying adulterated Sanqi powder. A total of 209 samples including pure and mixed samples, were prepared. Principal component analysis (PCA) was applied for exploratory analysis. The relief algorithm was used to rank all variables, based on which only the first 100 most informative variables were picked out for subsequent class-modeling. By optimizing the parameters such as the number of components, type I and type II errors, the final one-class models were constructed on the training set and evaluated on the test set. Such a procedure is simple and is more in line with actual need. The performance of the models is acceptable. The results indicate that NIR spectroscopy combined with class-modeling and relief-based variable selection is feasible for identifying the adulteration of Sanqi powder.

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging (fMRI) network. Finally, the result shows that the best model only is based on anatomical distance.

In this article we chronicle the development of the undergraduate statistical consulting course at Miami University, from canned to client-based projects, and argue that if the course is well designed with suitable mentoring, students can perform remarkably sophisticated analyses of real-world data problems that require solutions beyond the methods encountered in previous classes. We review the historical context in which the consulting class evolved, describe the logistics of implementing it, and review assessment and student reaction to the course. We also illustrate the types of challenging projects the students are confronted with via two case studies and relate the skills learned and reinforced in this consulting class model to the skills demanded in the modern statistical work force. This course also provides an opportunity to strengthen and nurture key points from the new American Statistical Association guidelines for undergraduate programs: namely, communicating analyses of real and complex data ...

Traditional analysis of food adulterations is usually focused on detection of specific adulterants that are known or suspected to exist. However, because there are various ways of adulterations and new adulterants are constantly emerging, the usefulness of the traditional analysis methods is limited. This paper investigated the feasibility of using near infrared (NIR) spectroscopy and chemometrics to detect a range of adulterants in a Chinese functional food, kudzu starch (KS). Pure KS objects ( n = 125) were collected from five main producing areas. Five potential adulterants were investigated, including an illegal but commonly used whitening agent, talcum powder, and four cheaper plant starches, namely, sweet potato, potato, maize and cassava starches. The doping levels were 2%, 4%, 6%, 10% and 20% (w/w) for plant starches and 0.5%, 1%, 2%, 5% and 10% (w/w) for talcum powder. A recently proposed one-class classifier, partial least squares class model (PLSCM) was used to estimate the distribution of authentic KS samples based on NIR spectra. The results indicate that taking second-order derivative (D2) spectra can effectively improve the PLSCM model specificity for detecting adulterants. D2-PLSCM can detect 0.5% or more of talcum powder and 4% or more of the four cheaper starches.

The short- and long-term efficacy of the Adlerian parent education program Systematic Training for Effective Parenting (STEP), which is the group-based parenting class model, was the focus of the current study in Lithuania. In the quasi-experimental research study, the outcome measures included parenting style, child behavior as perceived by parents, and knowledge related to parenting. In 2011–2012, 44 parenting groups were organized, which resulted in 348 program and 299 comparison group participants at posttest. In 3- to 4-month period, the follow-up assessment was completed by 38.8% of parents. Specifically, the findings indicated that the STEP program was efficacious for increasing maternal and paternal knowledge on parenting, decreasing maternal authoritarian and permissive parenting styles and parental negative perceptions of the target child’s behavior. The changes listed were found to be stable as reflected in the follow-up. The researchers believe that the current study with the sufficient empirical rigor expands the generalizability of the STEP program outcomes on mothers and fathers in North America, Lithuania as well as Europe. Furthermore, the results have finally provided the Lithuanian therapists, parent educators, and parents with an evidence-based parenting program as recommended by the Council of Europe.

In the present study, near-infrared spectroscopy (NIRS) was explored as a fast and reliable screening method for the detection of adulteration of skim milk powder (SMP). Sixty genuine SMP were adulterated with acid whey (1–25 % w/w), starch (2 and 5 %) and maltodextrin (2 and 5 %) for a total of 348 adulterated samples. Two chemometric approaches were employed. In the first approach, an untargeted one class model for genuine skim milk powder was developed by Soft Independent Modelling of Class Analogy. In the second approach, adulterant-specific regression models were developed to assess the amount of each adulterant by partial least square regression and principal component regression. The class modelling approach had the advantage that several adulterants could be detected with the same chemometric model, including situations where multiple adulterants are present in the test sample or where yet unknown adulterants are present. Regression models showed a better sensitivity with genuine SMP samples completely discriminated from samples adulterated with 5 % acid whey and 2 % of starch or maltodextrin. NIRS proved to be a useful tool for the rapid and cost-efficient untargeted and/or targeted detection of adulterations in SMP.

This article presents the authors' experience with flipping a fundamental concepts of nursing course for students in an undergraduate baccalaureate program. Authors describe implementing a flipped class, practical strategies to transform students' learning experience, and lessons learned. This article serves as a guide to faculty and programs seeking to develop and implement the flipped class model in nursing education.

The paper presents the results of research on possible application of an original clustering based mathematical description of gas adsorption processes to analysis of microporous structures of activated carbons. The LBET class model used is based on the classical BET approach to the multilayer adsorption process modelling, but it takes into account geometrical restrictions for the clusters size and a geometrical and energetic heterogeneity of surface. The effects of adhesive forces on the creation of second and higher layers are handled explicitly. An original fluid state model is used to make calculations possible for gas adsorption isotherms. Fitting of the LBET model to the empirical data is proposed to detect active constraints for multilayer adsorption and so to get information on the structure of micropores. Application of the formulae to the analysis of nitrogen and methane isotherms on an active carbon is discussed.

The combined use of data obtained from different analytical instruments is a complex problem. In this paper the potential of coupling three analytical techniques for building a class model for extra virgin (e.v.) olive oil from Liguria, was investigated. A sampling design for the Ligurian e.v. olive oil was developed by the selection of a representative subset from all possible e.v. olive oil samples of the Liguria region. Thus, in order to choose this subset with uniform distribution on the production area and representative of the production density, two algorithms for sampling have been used: Kennard–Stone and Potential Function. The samples were analysed by head-space mass spectrometry (electronic nose), UV–visible and NIR spectroscopy. In particular, the exceptional possibility provided by Chemometrics to effectively extract and combine (fusion) the information from these multivariate and non-specific data and to build a class model for Liguria e.v. olive oil was studied.

A key factor motivating the organisation of tutorial classes for adult learners, based on collaboration between the Workers’ Educational Association and universities in England and Wales, was a belief that they offered an educational vehicle for the promotion of liberal concepts of political citizenship. Attempts to disseminate this tutorial class model were not always successful, despite the promotional efforts of Albert Mansbridge and his fellow supporters. This article considers the educational and organizational characteristics of tutorial classes and makes a regional comparison of their meagre development in Scotland with their sustained growth elsewhere between 1907 and 1939. It identifies a complex weave of factors, related to both the nature of student demand and the funding of adult education, that inhibited the growth of tutorial classes in Scotland. These factors also provide insight into general barriers to the development of liberal adult education to promote citizenship in Scotland.

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised- and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

The focus of this study is to examine the reliability of models that are available in the open literature for simulating the interior moisture conditions, comparing the predicted interior relative humidity (RH) to measured data. Four models, for predicting the indoor RH in houses are tested against measured RH data for 25 houses. The models considered are primarily developed as design tools. The models tested are the European Indoor Class Model, the BRE model, and the ASHRAE 160P simple and intermediate models. The RH in each house is measured in two different locations producing 50 data sets. The ASHRAE intermediate model seemed to be the most robust exhibiting lower errors when compared to measured data. The European Indoor Class also performed well and can be used when data regarding moisture generation and/or air change rates is not available. As a design tool, however, it is not universally conservative in estimating the indoor RH. The BRE is problematic and generally exhibits large positive errors for most of the houses surveyed. It is found to be not reliable for the North American houses investigated in the comparisons. The ASHRAE simple model also exhibited large positive errors and does not trend well with the measured conditions. Models that greatly overestimate the design loads should be used with caution as they may lead to complicated inefficient designs.

We report preliminary results on the investigation of the hypothesis of the existence of latent classes in the total demand for days out in a vast section of the Italian Eastern Alps. Finite mixing is informed by socioeconomic variables and it is limited to the demand portion of a hurdle model of visitation. Gradually more flexible count models are estimated moving from the Poisson to the Negative Binomial -1 and -2, to the generalized negative binomial. The implications for expected consumers surplus, predicted visitation and choke prices are discussed for a plausible 2 class model where years of experience play an important role in class membership.

This historical study reveals that content priorities in local TV news can be traced to the first use of audience research by news consultants. It argues that "downmarket" standards were not a function of broadcasters' ratings demands. More elemental were characteristics of TV's majority viewers, who no longer merely watched TV news. Through audience research they became part of the news process.

Within the parameters of a class model this paper seeks to explain how the European labor movements developed against a background of expanding capitalist labor markets, rural - urban split, liberal political hegemony, conflicting and competing religious, ethnic and linguistic affiliation and state repression, from the mid nineteenth century until World War II. Stating that solidarity, organization and militancy are functions of urbanism, the paper raises the question of how different societies overcame the rural urban split. Besides noting that most countries in East and South Europe did not manage to cross the bridge, one may point out that extensive urbanization resulted from the exodus from the countryside simultaneous with the growth of industrial labor markets in towns and cities, and next, how the labor movement managed to expand trade unions outside the urban sector, and seeking electoral support among the agrarian population. Provided labor had considerable national political power, this turned out to be an effective policy; otherwise this organizational expansion would be confronted with violent opposition. Early industrialization often went hand in hand with liberal political domination, which occupied the social space and hence obstructed further diffusion by other organizations. Only external events such as a major war or an economic crisis were able to break these ties effectively. In West and North Europe repression was moderate and temporary, whereas the working classes in South and East Europe were exposed to repression and harassment by landlords, industrialists and state bureaucracies. Only fundamental changes in international and national power structures could bring advantages to the labor movement. From a theoretical angle it is the interaction between mode of production, political organization and state structure that best explains different forms of working-class integration in Europe.

Given a class model built from a dataset including labeled data, classification assigns a new data object to the appropriate class. In associative classification the class model (i.e., the classifier) is a set of association rules. Associative classification is a promising technique for the generation of highly accurate classifiers. In this article, we present a compact form which encodes without information loss the classification knowledge available in a classification rule set. This form includes the rules that are essential for classification purposes, and thus it can replace the complete rule set. The proposed form is particularly effective in dense datasets, where traditional extraction techniques may generate huge rule sets. The reduction in size of the rule set allows decreasing the complexity of both the rule generation step and the rule pruning step. Hence, classification rule extraction can be performed also with low support, in order to extract more, possibly useful, rules.

Early literacy interventions have been critiqued for their propensity to remove “reading problems” from classroom teachers’ responsibility. In the current study, classroom teachers were trained so that they could provide specialized early intervention lessons to at‐risk students in their own classes. The results of the project compare favorably with a “pull‐out” model of early literacy intervention. In contrast to pull‐out approaches, this “push‐in” model of early literacy intervention may have pervasive effects on classroom teachers’ entire teaching repertoires.

Inclusive and collaborative intervention settings represent a restructuring of service delivery for students with special needs. This article contrasts traditional versus collaborative service delivery settings designed for students with language impairments. The Class Within a Class model (CWC) of collaborative practice is discussed and effective practices for adapting this model to use for a language intervention is explained. The described case study contrasts language gains when service delivery is changed from a pull-out model to the CWC model. Significantly more language gains were obtained when services were delivered through CWC.

We consider the single item newsboy problem, where the item can be sold to different demand classes at different prices. The demands are realized sequentially over time. That is, the newsboy purchases newspapers at the beginning of the day and sells them in the morning and in the afternoon with different prices. We analyze two cases where the prices are either decreasing or increasing; the former case applies, for example, to fashion goods retailing, while the latter to airlines and hotels. In the decreasing price case, we find the optimal order quantity to maximize the expected profit with independent multiple demands. We show numerically that aggregating the multiple demands with a single average price or applying the single demand newsboy model separately to multiple demand classes may lead to large sub-optimality. In the increasing price case, we analyze a two demand class model in which a fraction of the unsatisfied lower fare demand diverts to the high fare class, thus causing dependent sales. We follow a policy of protecting the sales in the higher fare class by limiting the sales in the lower fare class. We derive both the fare allocation limit and the initial capacity, and discuss managerial implications. For both models, we give bounds on the optimal order quantity.

This paper reconsiders the two/one-class model of economic growth, attributable to Kaldor, Pasinetta, Meade, Samuelson, Modigliani, and Sato, under the assumptions that part of corporate profits may not be distributed as dividends. Using results of M. Baranzini (1991), the distribution of property between the workers and capitalists is related to the rates of return on capital. Implications of a positive retention ratio are briefly considered. Copyright 1995 by Royal Economic Society.

We propose function for candidate discrimination, i.e., suspect ordering during model-based diagnosis. Function offers advantages over structure and fault probabilities currently being used for candidate discrimination. It is readily available from device design, unlike fault probabilities, which are hard to obtain. Function-based discrimination is not dependent on the topology of the device, unlike structure-based discrimination. We propose classes as a scheme for representation of function. As part of classes, we define a set of function primitives and provide a framework for identifying the functions of components and subsystems of a device. The representation scheme is domain independent. We propose a function-based technique for candidate discrimination called the default order technique, and outline a diagnosis algorithm that applies the technique to the class model of a device. Function-based diagnosis is in addition to and as a supplement for model-based diagnosis based on behavior and structure. ...

Interest often focuses on estimating sensitivity and specificity of a group of raters or a set of new diagnostic tests in situations in which gold standard evaluation is expensive or invasive. Various authors have proposed semilatent class modeling approaches for estimating diagnostic accuracy in this situation. This article presents imputation approaches for this problem. I show how imputation provides a simpler way of performing diagnostic accuracy and prevalence estimation than the use of semilatent modeling. Furthermore, the imputation approach is more robust to modeling assumptions and, in general, there is only a moderate efficiency loss relative to a correctly specified semilatent class model. I apply imputation to a study designed to estimate the diagnostic accuracy of digital radiography for gastric cancer. The feasibility and robustness of imputation is illustrated with analysis, asymptotic results, and simulations.

In his nice paper (Mykhopadhyay, 1982) as well as in his significant monograph (Mykhopadhyay & Solanky, 1994) N. Mykhopadhyay considers the following application of STEIN's two-stage procedure: Suppose that (X 1,..., Xn ) T , n = 1, 2,..., is n-dimensional normal with mean vector µ = µ l and dispersion matrix Σ n =σ 2(ρij ) with ρij = 1, ρij = ρ *, i ≠ j = 1,..., n where (µ, Σ, ρ) ∈ ℝ × ℝ+ × (-1, 0); this is called the intra-class model. For given d > 0 and α ∈ (0, 1) one wants to construct a (sequential) confidence interval I for µ having width 2d and confidence coefficient at least (1 - α). It is claimed that where N is determined, according to Stein's two-stage procedure (Stein, 1945), as where m ⩾ 2 is the first stage sample size and denotes the sample variance, fulfills this aim.

Hamada showed that the income transfer from capitalists to workers does not increase the income of workers in the long run. His result is based on the nonlinearity of the saving functions. If the saving function of capitalists is concave (or convex), the maximum of workers' income is attained by the positive (or the negative) transfer. There exists a case where the after-tax income of capitalists increases by a positive transfer. The global maximum of capitalists' income is attained when workers' income is all transferred to capitalists, while the global maximum of workers' is attained when capitalists enjoy positive income.