Functional Structure and Approximation in Econometrics (book front matter)

This is the front matter from the book, William A. Barnett and Jane Binner (eds.), Functional Structure and Approximation in Econometrics, published in 2004 by Elsevier in its Contributions to Economic Analysis monograph series. The front matter includes the Table of Contents, Volume Introduction, and Section Introductions by Barnett and Binner and the Preface by W. Erwin Diewert. The volume contains a unified collection and discussion of W. A. Barnett's most important published papers on applied and theoretical econometric modelling.consumer demand, production, flexible functional form, functional structure, asymptotics, nonlinearity, systemwide models

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

On the hedonic modelling of land prices

In this study hedonic modelling methods beyond the ordinary least squares estimator are investigated in explaining and predicting the land prices in the two submarkets (Espoo and Nurmijärvi) of the Finnish land markets. The first paper deals with the estimation of several parametric hedonic models, including dynamic responses, using recursive estimation technique. The second paper examines the applicability of semiparametric structural time series methods to the optimal estimation of spatio-temporal movements of land prices. The third paper focuses on the robust nonparametric estimation using local polynomial modelling approach in explaining and predicting the land prices. The fourth paper investigates flexible wavelet transforms in the estimation of long-run temporal land price movements (cycles and trends). The final fifth paper uses robust parametric estimator, the three-stage MM-estimator, to explicitly address the problem of outlying and influential data points. The key observation of this study is that there is much scope for methods beyond the ordinary least squares estimator in explaining and predicting the land prices in local markets. This is especially true in the submarket of Espoo, where the use of unconventional methods of the study showed that significant improvements could be achieved in hedonic models' explanatory power and/or predictive validity when the methods of this research are used instead of the orthodox least squares estimator. In the Espoo case structural time series models, local polynomial regression and robust MM-estimation all generated more precise results in terms of post-sample prediction power than the conventional least squares estimator. The empirical experimentation quite strongly indicated that the determination of land prices in the municipality of Nurmijärvi could be best explained by the use of unobserved component models. The flexible local polynomial modelling and three-stage MM-estimation surprisingly added no value in terms of greater post-sample precision in the Nurmijärvi case.Tässä tutkimuksessa tarkastellaan sellaisia hedonisia mallintamismenetelmiä, jotka yleistävät tavallisen pienimmän neliösumman mukaista ratkaisua, kun selitetään ja ennustetaan maanhintoja kahdella osamarkkina-alueella (Espoo ja Nurmijärvi) Suomen maamarkkinoilla. Ensimmäinen artikkeli tarkastelee erilaisten parametristen mallien estimointia käyttämällä rekursiivista estimointitekniikkaa. Toinen artikkeli tutkii semiparametristen rakenteellisten aikasarjamallien soveltuvuutta ajallis-paikallisten maanhintavaihteluiden optimaalisessa estimoinnissa. Kolmas artikkeli keskittyy vikasietoiseen ja ei-parametriseen estimointiin käyttämällä paikallisia polynomimalleja, kun selitetään ja ennustetaan maanhintoja. Neljäs artikkeli tutkii joustavia aalloke-muunnoksia pitkän ajanjakson maanhintojen vaihteluiden (syklien ja trendien) estimoinnissa. Viimeinen viides artikkeli käyttää vikasietoista parametrista estimaattoria, kolmivaiheista MM-estimaattoria, vähentämään mallintamisessa ilmenevien poikkeavien ja vaikutusvaltaisten havaintopisteiden negatiivinen vaikutus. Tutkimuksen avainhavainto on, että tutkimuksessa tarkasteltuja epästandardeja menetelmiä voidaan soveltaa hyvin käytännön ongelmaratkaisutilanteissa, kun selitetään ja ennustetaan maanhintoja paikallisilla markkinoilla. Tämä pätee erityisesti Espoon hinta-aineistolla, jossa epästandardien menetelmien käyttö johti hedonisiin hintamalleihin, jotka omasivat huomattavasti korkeamman selitysvoimakkuuden ja/tai ennustustarkkuuden kuin tavallisen pienimmän neliösumman mukainen ratkaisu. Espoon osamarkkinoiden tapauksessa rakenteelliset aikasarjamallit, vikasietoinen paikallinen regressioanalyysi ja vikasietoinen MM-estimointi tuottivat tarkempia tuloksia kuin perinteinen pienimmän neliösumman mukainen keino, kun estimoitujen mallien hyvyyttä arviointiin ennustustarkkuuden mielessä eri kriteereillä. Empiirinen tutkimus indikoi varsin voimakkaasti, että Nurmijärven osamarkkinoiden tapauksessa maanhinnan muodostus voitiin parhaiten selittää käyttämällä rakenteellisia aikasarjamalleja. Sen sijaan joustavat polynomimallit ja MM-estimointi eivät tuoneet lisäarvoa mallien paremman ennustustarkkuuden valossa Nurmijärven hinta-aineistolla.reviewe

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Change-point Problem and Regression: An Annotated Bibliography

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

Intra and inter-brand calibration transfer for near infrared spectrometers

Robust modeling methods were implemented for the transfer of near-infrared calibration models in intra and inter-brand situations. A network of four instruments from two brands (Foss Infratecs and Bruins OmegAnalyzerGs) was used to implement spectral pretreatment methods, local and variable selection techniques, and orthogonal methods to transfer protein, oil, and linolenic acid models across instruments of the same brand and across instruments of different brands. A total of fifty seven techniques were implemented among which spectral filtering methods based on the smoothing of high frequency components of Fourier and wavelet transforms. A new approach to local similarity was introduced. Results showed that the effectiveness of the various methods was instrument, parameter, and validation set dependent. In some situations, no differences could be observed between master and secondary unit predictions. Local methods appeared to be the weakest methods, most likely due to a problem of over-fitting (specialization) of the calibration set. The transfer of calibrations across brands was possible with performances similar, or better, than in intra-brand calibration transfer

Off to a Poor Start: The Role of Childhood Adversity in Employee Burnout, Turnover, Commitment, and Counterproductive Behavior

Despite the abundance of interdisciplinary research on childhood adversity, the topic has been largely neglected as it relates to occupational health. However, this understudied area has important implications for both research and practice. Using the Matthew Effect and Conservation of Resources Theory as a foundation, the present study investigated the relationship between childhood adversity and adult work-related outcomes. The literature on childhood adversity suggests that adverse experiences as a child such as abuse, or poverty accumulate and result in adults who are at a disadvantage in many ways such as in their interpersonal relationships, occupational and educational success, and mental and physical health. These individuals have fewer resources as they enter the workforce and are often unable to cope with the demands of life. Therefore, the present study hypothesized that because of this cumulative disadvantage, these individuals may be more likely to experience poor health-related work outcomes. In general, the results of this study indicate that individuals who have experienced childhood adversity are more likely to burnout, have intentions to turnover, and engage in counterproductive work behavior. Further, childhood poverty, emotional neglect, being in an unsafe home, household substance abuse, household depression, and being bullied were all associated with lower levels of both affective organizational and occupational commitment. The findings of this study provide valuable insight into the long-term implications of an employees’ past on their present employment situation and provide a foundation for future research to build

Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre-processing technique