Process Monitoring with Multivariate p -Control Chart

We assume that the operator is interested in monitoring a multinomial process. In this case the items are classified into (k+1) ordered distinct and mutually exclusive defect categories. The first category is used to classify the conforming defect-free items, while the remainingkcategories are used to classify the nonconforming items inkdefect grades, with increasing degrees of nonconformity. Usually the process is said to be capable if the overall proportion of nonconforming items is very small and remains low, or declines over time. Nevertheless, since we classify the nonconforming items intokdistinct defect grades, the operator can also evaluate the overall level of defectiveness. This quality parameter depends on thekdefect categories. Furthermore, we are interested in evaluating, over time, the proportion of nonconforming items in each category as well as the overall level of defectiveness. To achieve this goal, we propose (i) a normalized index that can be used to evaluate the capability of the process in terms of the overall level of defectiveness, and (ii) a two-sided Shewhart-type multivariate control chart to monitor the overall proportion of nonconforming items and the corresponding defectiveness level

Hierarchical categorization of coarticulated phonemes: A theoretical analysis

This article is concerned with the question of how listeners recognize coarticulated phonemes. The problem is approached from a pattern classificationperspective. First, the potential acoustical effects of coarticulation are defined in terms of the patterns that form the input to a classifier.Next, a categorization model called HICAT is introduced that incorporates hierarchical dependencies to optimally dealwith this input. The model allows the position, orientation, and steepness of one phoneme boundary to depend on the perceivedvalue of a neighboring phoneme. It is argued that, if listeners do behave like statistical pattern recognizers, they may use the categorization strategies incorporated in the model. The HICAT model is compared with existing categorizationmodels, among which are the fuzzylogical model of perception and Nearey’s diphone-biased secondary-cuemodel. Finally, a method is presented by which categorization strategies that are likely to be used by listeners can be predicted from distributions of acoustical cues as they occur in natural speech

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Perceived neighbourhood quality and adult health status: new statistical advices useful to answer old questions?

Interest in the quantitative effects of neighbourhood characteristics on adult health has recently increased in social epidemiology. Particularly, investigations concern the statistical influence on health of several individual demographic and socioeconomic characteristics and of neighbourhood characteristics as perceived by respondents. We analyze these issues within an original conceptual framework and employing statistical models unusual in this context. We use data collected in the Los Angeles Family and Neighbourhood Survey (L.A.FANS) to model the number of hospital admissions occurred to each individual as a function of some individual and neighbourhood characteristics, the latter being related to the individual perceptions about the neighbourhood he lives in. We employ generalized additive models with different distributional assumptions: Poisson, Negative Biomial and Zero Inflated Poisson (ZIP). Such models allow us to estimate (through spline functions) potential non linear effects of the covariates on the response. Moreover, non standard representations are used to overcome difficulties in interpreting the results for ZIP models. It turns out that perceived neighbourhood characteristics, and in particular the perception of social cohesion, have a significant effect after controlling for individual characteristics relevant to hospital admissions frequency. From a modeling point of view ZIP and Negative binomial models prove to be superior to standard Poisson model. We have confirmed the role of the neighbourhood where an individual lives in determining his health status. A strength of this analysis is that, due to the choice of the neighbourhood characteristics to be included in the model, the results do not depend of a particular definition of neighbourhood (which is traditionally based on administrative boundaries), since each individual refers his perceptions to his personal definition of it

Misconceptions in rational numbers, probability, algebra, and geometry.

In this study, the author examined the relationship of probability misconceptions to algebra, geometry, and rational number misconceptions and investigated the potential of probability instruction as an intervention to address misconceptions in all 4 content areas. Through a review of literature, 5 fundamental concepts were identified that, if misunderstood, create persistent difficulties across content areas: rational number meaning, additive/multiplicative structures, absolute/relative comparison, variable meaning, and spatial reasoning misconceptions. Probability instruction naturally provides concrete, authentic experiences that engage students with abstract mathematical concepts, establish relationships between mathematical topics, and connect inter-related problem solving strategies. The intervention consisted of five probability lessons about counting principles, randomness, independent and dependent event probability, and probability distributions. The unit lasted approximately two weeks. This study used mixed methodology to analyze data from a randomly assigned sample of students from an untreated control group design with a switching replication. Document analysis was used to examine patterns in student responses to items on the mathematics knowledge test. Multiple imputation was used to account for missing data. Structural equation modeling was used to examine the causal structure of content area misconceptions. Item response theory was used to compute item difficulty, item discrimination, and item guessing coefficients. Generalized hierarchical linear modeling was used to explore the impact of item, student, and classroom characteristics on incorrect responses due to misconceptions. These analyses resulted in 7 key findings. (1) Content area is not the most effective way to classify mathematics misconceptions; instead, five underlying misconceptions affect all four content areas. (2) Mathematics misconception errors often appear as procedural errors. (3) A classroom environment that fosters enjoyment of mathematics and value of mathematics are associated with reduced misconception errors. (4) Higher mathematics self confidence and motivation to learn mathematics is associated with reduced misconception errors. (5) Probability misconceptions do not have a causal effect on rational numbers, algebra, or geometry misconceptions. (6) Rational number misconceptions do not have a causal effect on probability, algebra, or geometry misconceptions. (7) Probability instruction may not affect misconceptions directly, but it may help students develop skills needed to bypass misconceptions when solving difficult problems

Time Series Modelling

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples