Inclusion Of Explanatory Variables Research Articles

A A Review of Bridge Management System: Deterioration Models

In the last decades, in a large part of developed countries, a strong investment was made in the construction of infrastructures, namely bridges. However, the state of the road bridges changes with age due to several factors, which cause a progressive worsening of their degradation and a consequent decrease in the strength of the structure, if measures are not implemented that somehow cancel or counteract these effects, being of great importance correct management of the conservation status of these works of art. Good managerial performance is essential especially in view of the limited financial resources or under the bias of their best use. Through computerized bridge management systems or “Bridge Management Systems”, deterioration rates are introduced through algorithms that may vary according to the type of structure, its location, constituent material, or even the environment in which it is inserted, providing important subsidies for decision making. Deterioration Models that may be deterministic, such as least squares-based regression, or probabilistic, such as Markov chains are considered in this review paper. Other methodologies use artificial intelligence processes (natural algorithms or neural networks). The objective of the article is to describe the existing techniques, evaluating their limitations, specificities, and potential. Based on the awareness of the importance of deterioration models, it is possible to verify the fundamental role of routine inspections for the management of construction parks. The inspection of bridges can develop beyond the simple verification of the condition of the works in the present, providing subsidies for a better understanding of the behavior of these works over time. In the same way, inspection methods could provide for the collection of data relevant to the manager's knowledge containing information about possible decisive agents in the deterioration rates. In the long term, the development and inclusion of explanatory variables in bridge management could bring great benefits to the industry. The development of mechanistic models used in conjunction with deterioration models could provide accurate results in predicting the state of condition. In addition, the development of these models would provide benefits in understanding the mechanisms of deterioration of the bridge components.

A product-multinomial framework for categorical data analysis with missing responses

We extend the multinomial modeling scenario for the analysis of categorical data with missing responses described by Paulino (1991, Brazilian Journal of Probability and Statistics, 5, 1-42) to the product-multinomial setup so that the inclusion of explanatory variables is allowed. Assuming an ignorable missing data mechanism, linear and log-linear models may be fitted via maximum likelihood. Weighted least squares methodology may as well be used to fit more general functional linear models, if a missing completely at random mechanism is assumed. We also consider a hybrid approach, where any missingness process is fitted by maximum likelihood in a first step, and the estimated marginal probabilities of categorization and their covariance matrix are used in a second stage to fit the model via weighted least squares, in the spirit of functional asymptotic regression methodology. Goodness-of-fit tests are present, and the methodology is illustrated via two data sets. All the methods were computationally implemented via subroutines written in R.

The Reaction Function of Three Central Banks of Visegrad Group

The aim of our paper is to formulate and empirically verify the simple backward looking econometric model of the monetary policy rule, which would be able to describe the development of monetary policy rate, namely only on the basis of statistically measured and at the given time available information. We focus on the Czech National Bank, the National Bank of Poland and the Magyar Nemzeti Bank in the period of January 1999 to April 2012. In the present paper we discuss some methodological problems associated with the ex-post empirical verifi cation of the central bank’s monetary policy rule. We construct an empirical model of the monetary policy rule, justify the choice and the inclusion of explanatory variables, analyse the statistical properties of time series, and verify the alternative forms of econometric models. Our analysis showed that the development of monetary policy rate in the reporting period can be explained by the past and present development of four explanatory variables: yearly infl ation rate, exchange rate, ECB main refi nancing rate and growth rate of M2. The annualized infl ation rate proved to be statistically insignifi cant in the model. We fi nd interesting that the statistical quality of the estimated model was further increased after a six-month lag of the annual infl ation rate added to the model.

Je možné předpovídat repo sazbu ČNB na základě zpět hledícího měnového pravidla?

The aim of our paper is to formulate and empirically verify the simple backward looking econometric model of the monetary rule, which would be able to describe the development of CNB repo rate, namely only on the basis of statistically measured and in the given time available information. We focus on the period after 1998, when the CNB's inflation targeting policy is implemented and the repo rate (14 days) plays the role of the monetary policy rate. In the paper we discuss some methodological problems associated with the ex post empirical verification of the central bank monetary rule. We construct an empirical model of the monetary rule, justify the choice and the inclusion of explanatory variables, we analyze the statistical properties of time series and verify the alternative forms of econometric models. Our analysis showed that the development of CNB repo rate in the reporting period can be explained by the past and present evolution of three explanatory variables: the yearly inflation rate, the exchange rate and the ECB repo rate. The annualized inflation rate proved to be statistically insignificant in the model. We find interesting that the statistical quality of the estimated model was further increased after a six-month delay of the yearly inflation rate. The obtained results indicate that in determining the CNB repo rate the expected future level of the yearly inflation rate does not play important role and the last yearly inflation rate is more important than its present level.

Iterative Probabilistic Approach for Selection of Time-Varying Model Classes

In this study, an adaptive probabilistic algorithm was developed for the selection of the most plausible Kalman filter based time-varying statistical model by using Bayesian approach. The method was validated by a case study involving the prediction of the sample time series recorded in Macau between 2001 and 2005. Two types of statistical models, namely the time-varying autoregressive model of order p, abbreviated as the TVAR (p) model, and the timevarying autoregressive model with exogenous inputs, abbreviated as the TVAREX model, were adopted to predict the sample time series. By judging upon the model occurring plausibility conditional on the measured data, it was found that using a longer past history did not guarantee better prediction performance. On the contrary, inclusion of explanatory variables which reflect the mechanism of the physical problem can better capture the actual system.

Crown condition as a function of soil, site and tree characteristics

One of the aims of this work is to describe how the target variable “tree vitality” in terms of needle loss is affected by other explanatory variables. To describe such a relationship in a realistic way, we use generalized additive mixed models (GAMMs) which allow to take spatial correlation of the data into account and in addition allow the inclusion of explanatory variables as predictors with the possibility of having non-linear effects. The GAMMs are fitted in a Bayesian framework using Markov chain Monte Carlo techniques. Data are available for two years 1988 and 1994. We select a set of best explanatory variables from a large set of variables including tree-specific variables, such as species, age, nutrients in the needles and site-specific variables such as altitude, relief type, soil depth and content of different nutrients in the top soil. In the two models for 1988 and 1994, different sets of explanatory variables were selected as best predictors. In both models, the effects of explanatory variables allowed a plausible interpretation. For example, the site-specific variables such as relief and soil depth were significant predictors, since these factors determine how well water and nutrient supply is balanced at a specific site. The selected sets of explanatory variables differed between 1988 and 1994, giving an indication of a possible change in the main causes of forest deterioration between 1988 and 1994. From the set of nutrient variables measured in the soil and in the needles, in 1988 altitude a.s.l. and magnesium supply were among the explanatory variables, in 1994 a combination of Al in the soil and the N/K-ratio (in the needles) was selected in the model. In 1988 altitude a.s.l. was among the most important predictors in the model. This is in contrast to 1994 where altitude was not selected. This may have to do with the fact that in the early phase of forest health monitoring (1988) one of the main causes of forest deterioration was magnesium deficiency. Later on this may have changed to a combination of soil acidification and nitrogen eutrophication. Thus by using an adequate model such as the GAMM, sets of explanatory variables for needle loss may be identified. By fitting two GAMMs, with different sets of “best” predictors, at two time points 1988 and 1994, we can detect changes in these sets of “best” predictors over time. This allows us to use the monitoring data with the tree vitality indicator crown condition/needle loss as a tool for forest health management, which may involve decisions about concrete counter measures like e.g. forest liming.

How (Not) to Lie with Spatial Statistics

How (Not) to Lie with Spatial Statistics

Estimating Arbitrator's Hidden Judgement in Final Offer Arbitration

In Final Offer Arbitration the arbitrator has to choose between two prices – salary offer a and demand b, presented by two negotiating sides. This situation is common e.g., in Major League Baseball since 1974, for players with at least three years experience. We model the arbitrator's decision process as follows. First, the arbitrator chooses an appropriate sum Z. Then he selects the one among a and b which is closer to the ‘proper sum’Z. The final amount selected is Y . Based on the history of n cases, the information available is the triplets {(Y i, a i, b i) : i= 1, 2, . . . n}(the {Z i} are hidden). It is assumed that the {Z i} are random variables with a distribution which depends on an unknown parameter q and the challenge is to estimate q. Furthermore, since each case had different merits and characteristics, the resulting distribution is case-specific. Thus, our model allows the inclusion of explanatory variables. The statistical algorithm which we shall use is the Expectation-Maximization(EM) algorithm. In the paper, the statistical model is introduced in detail and the application of the EM algorithm to the available data is explained. Two numerical examples illustrate the use of the EM algorithm in estimating the arbitrator's hidden judgements.

Automatic methods for generating seismic intensity maps

For many years the modified Mercalli (MM) scale has been used to describe earthquake damage and effects observed at scattered locations. In the next stage of an analysis involving MM data, isoseismal lines based on the observations have been added to maps by hand, i.e. subjectively. However a few objective methods have been proposed (by e.g. De Rubeis et al., Brillinger, Wald et al. and Pettenati et al.). The work presented here develops objective methods further. In particular the ordinal character of the MM scale is specifically taken into account. Numerical smoothing is basic to the approach and methods involving splines, local polynomial regression and wavelets are illustrated. The approach also allows the inclusion of explanatory variables, for example site effects. The procedure is implemented for data from the 17 October 1989 Loma Prieta earthquake.

Automatic methods for generating seismic intensity maps

For many years the modified Mercalli (MM) scale has been used to describe earthquake damage and effects observed at scattered locations. In the next stage of an analysis involving MM data, isoseismal lines based on the observations have been added to maps by hand, i.e. subjectively. However a few objective methods have been proposed (by e.g. De Rubeiset al., Brillinger, Waldet al.and Pettenatiet al.).The work presented here develops objective methods further. In particular the ordinal character of the MM scale is specifically taken into account. Numerical smoothing is basic to the approach and methods involving splines, local polynomial regression and wavelets are illustrated. The approach also allows the inclusion of explanatory variables, for example site effects. The procedure is implemented for data from the 17 October 1989 Loma Prieta earthquake.

Changes in Attitudes toward Women′s Emancipation in the Netherlands Over Two Decades: Unraveling A Trend

Attitudes toward women′s emancipation in the Netherlands have been increasingly favorable since 1970. Decomposition of the trend indicates that intracohort change has a larger share in the general trend than cohort succession. Reduction in estimated effect of cohort succession by the inclusion of explanatory variables is larger than in intracohort change, but neither of the two components disappear. Education is the most important variable underlying cohort effects. Both genders have on average adopted more favorable attitudes toward women′s emancipation. Having a job is positively related to attitudes toward emancipation among women. Linear effects of aging are excluded from analyses on theoretical grounds. An age-related pattern was found, however, among people under 25 years old. This result is discussed in terms of the formative period concept. It is argued that the trend consists predominantly of period effects, which are constituted by actual changes in women′s positions in society.

Citizen Response to Proposals for Metropolitan Governmental Integration: Toward a Conceptual Model

Past studies have attempted to identify the correlates of citizen response to proposals for governmental integration. The inconsistency of the findings of such research indicates the need to formulate a general conceptual model in order to assess the interrelationships among the variables previously considered and to estimate the adequacy of current efforts in order to explain response to governmental integration proposals. Such a general model is formulated and tested using survey data. The inadequacy of the general model used in explaining citizen responses to a proposed type of governmental integration, city-county consolidation, by respondents living in unincorporated areas adjacent to the City of Atlanta, suggests the need for development and inclusion of explanatory variables designed to represent contextual variations specific to a particular metropolitan setting.