Registration of longitudinal data by means of musical notation

There is a lack of good registration methods for complex longitudinal data. Cumulating longitudinal data into cross-sections, even repeated ones, in order to allow for statistical analysis means considerable distortion of data; if they interact with each other, this is not possible to observe directly and the time factor is not taken into account to its full value, if at all. In this context, music may be defined as a complicated development in the course of time. Music is usually represented by musical notation which, if not ideal, means that we already possess a high precision registration method for complicated temporal developments. Musical notation used for registration of other longitudinal variables than musical ones allows for a multitude of qualitative variables being taken into account simultaneously and a high precision regarding the time factor. It should be no more difficult to analyse statistically a longitudinal pattern than a surface pattern.     

Econometric models based on count data. Comparisons and applications of some estimators and tests

This paper deals with specification, estimation and tests of single equation reduced form type equations in which the dependent variable takes only non-negative integer values. Beginning with Poisson and compound Poisson models, which involve strong assumptions, a variety of possible stochastic models and their implications are discussed. A number of estimators and their properties are considered in the light of uncertainty about the data generation process. The paper also considers the role of tests in sequential revision of the model specification beginr ing with the Poisson case and provides a detailed application of the estimators and tests to a model of the number of doctor consultations.     

On some applications of Hadamard matrices

Modern communications systems are heavily reliant on statistical techniques to recover information in the presence of noise and interference. One of the mathematical structures used to achieve this goal is Hadamard matrices. They are used in many different ways and some examples are given. This paper concentrates on code division multiple access systems where Hadamard matrices are used for user separation. Two older techniques from design and analysis of experiments which rely on similar processes are also included. We give a short bibliography (from the thousands produced by a google search) of applications of Hadamard matrices appearing since the paper of Hedayat and Wallis in 1978 and some applications in telecommunications.     

Extracting a common stochastic trend: Theory with some applications

This paper investigates the statistical properties of estimators of the parameters and unobserved series for state space models with integrated time series. In particular, we derive the full asymptotic results for maximum likelihood estimation using the Kalman filter for a prototypical class of such models—those with a single latent common stochastic trend. Indeed, we establish the consistency and asymptotic mixed normality of the maximum likelihood estimator and show that the conventional method of inference is valid for this class of models. The models we explicitly consider comprise a special–yet useful–class of models that may be employed to extract the common stochastic trend from multiple integrated time series. Such models can be very useful to obtain indices that represent fluctuations of various markets or common latent factors that affect a set of economic and financial variables simultaneously. Moreover, our derivation of the asymptotics of this class makes it clear that the asymptotic Gaussianity and the validity of the conventional inference for the maximum likelihood procedure extends to a larger class of more general state space models involving integrated time series. Finally, we demonstrate the utility of this class of models extracting a common stochastic trend from three sets of time series involving short- and long-term interest rates, stock return volatility and trading volume, and Dow Jones stock prices.     

A collective labor supply model with complementarities in leisure: Identification and estimation by means of panel data

We present a novel identification strategy for a collective labor supply model that allows for complementarities in leisure (i.e., individuals may enjoy leisure more in company of their spouse). Individual preferences and the Pareto weights (which capture the intra-household bargaining process) are identified by making use of panel data with couples and individuals who became a widow(er) in the observation period, along with the assumption that an individual's preferences can only change in a particular manner after the spouse's death. The change in preferences comes from changes in observable variables that can be controlled for (like mental health) and from the loss of the possibility to jointly enjoy leisure after the couple's dissolution. We apply the model to American households coming from the first nine waves of the Health and Retirement Study (1992-2008) and show that complementarities in leisure are indeed important when modeling spouses' labor supply choices.     

Analysis of longitudinal data using the hierarchical linear model

The hierarchical linear model in a linear model with nested random coefficients, fruitfully used for multilevel research. A tutorial is presented on the use of this model for the analysis of longitudinal data, i.e., repeated data on the same subjects. An important advantage of this approach is that differences across subjects in the numbers and spacings of measurement occasions do not present a problem, and that changing covariates can easily be handled. The tutorial approaches the longitudinal data as measurements on populations of (subject-specific) functions.     

High precision registration of complicated longitudinal and cross-sectional data

In the article are given examples showing how, by using musical notation in registering (complicated) developments, considerably more qualitative variables may be taken into account than we were used to; furthermore, the temporal element may be represented with a degree of precision that corresponds to our needs. In case of cross-sectional data modifications of musical notation may be used. However, we have to recognize that there are also variables, maybe important ones, that — for several reasons — cannot be registered with a very high precision. Other complications are hinted at. The method described is rather a working method by means of which to improve the basis for a further quantitative analysis than a method suitable for showing final research results.     

Robust analysis of longitudinal data with nonignorable missing responses

We encounter missing data in many longitudinal studies. When the missing data are nonignorable, it is important to analyze the data by incorporating the missing data mechanism into the observed data likelihood function. The classical maximum likelihood (ML) method for analyzing longitudinal missing data has been extensively studied in the literature. However, it is well-known that the ordinary ML estimators are sensitive to extreme observations or outliers in the data. In this paper, we propose and explore a robust method, which is developed in the framework of the ML method, and is useful for downweighting any influential observations in the data when estimating the model parameters. We study the empirical properties of the robust estimators in small simulations. We also illustrate the robust method using incomplete longitudinal data on CD4 counts from clinical trials of HIV-infected patients.     

Improving discrimination in data envelopment analysis: some practical suggestions

In some contexts data envelopment analysis (DEA) gives poor discrimination on the performance of units. While this may reflect genuine uniformity of performance between units, it may also reflect lack of sufficient observations or other factors limiting discrimination on performance between units. In this paper, we present an overview of the main approaches that can be used to improve the discrimination of DEA. This includes simple methods such as the aggregation of inputs or outputs, the use of longitudinal data, more advanced methods such as the use of weight restrictions, production trade-offs and unobserved units, and a relatively new method based on the use of selective proportionality between the inputs and outputs.     

Minimizing maximum lateness on one machine: computational experience and some applications

We consider the problem of scheduling jobs on a single machine subject to given release dates and precedence constraints, in order to minimize maximum lateness. The algorithms of B aker and S u (Naval Res. Logist. Quart. 21 (1974) 171–176) and of M c M ahon and F lorian (Operations Res. 23 (1975) 475–482) for the problem without precedence constraints are extended to the general case. An extensive computational comparison establishes the superiority of the latter algorithm. We describe applications to the theory of job-shop scheduling and to a practical scheduling situation.     

FRED-SD: A real-time database for state-level data with forecasting applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor-market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially disaggregated data tend to have higher predictive ability for industrially diversified states. For national-level data, we find that forecasting and aggregating state-level data can outperform a random walk but not an autoregression. We compare these real-time data experiments with forecasting experiments using final-vintage data and find very different results. Because these final-vintage results are obtained with revised data that would not have been available at the time the forecasts would have been made, we conclude that the use of real-time data is essential for drawing proper conclusions about state-level forecasting models.     

Estimation of models with grouped and ungrouped data by means of “2SLS”

This paper deals with a special case of estimation with grouped data, where the dependent variable is only available for groups, whereas the endogenous regressor(s) is available at the individual level. By estimating the first stage using the available individual data, and then estimating the second stage at the aggregate level, it might be possible to gain efficiency relative to the OLS and 2SLS estimators that use only grouped data. We term this the mixed-2SLS estimator (M2SLS). The M2SLS estimator is consistent and asymptotically normal. We also provide a test of efficiency of M2SLS relative to OLS and “2SLS” estimators.     

A review of some unusual applications of signal detection theory

Quality & Quantity -     

Visual displays: analytical study and applications to graphs and real data

Principal axis methods such as principal component analysis (PCA) and correspondence analysis (CA) are useful for identifying structures in data through interesting planar graphic displays. However, some kinds of data sets can be dealt alternatively with PCA or CA. This paper focuses on methods, such as PCA and CA, and on visual displays. Our aim is to illustrate the implications for a potential user of selecting either method, and its advantages and disadvantages, from an applied point of view. This is a matter covered broadly in textbooks and elsewhere considering theoretical arguments. Our purpose is to contribute to the comparison between these methods, over the same data set, in order to illustrate them for the practitioner. In the first part of this paper we present a novel analytical study of a binary matrix associated with a non-oriented axis-symmetric graph and show that CA outperforms standardized PCA for the reconstitution and visualization of such kind of graphs. In the second part we present a case using real data dealing with the distribution of employees in different economic sectors for the countries of the European Union, analyzed by means of standardized PCA and two-way CA, in order to see the differences between the two methods in practice.     

数据挖掘在高校后勤中的应用

如何快速有效地从日益膨胀的高校后勤数据中,挖掘出潜在的、有价值的信息,使之有效地在高校后勤管理、经营服务和决策中发挥作用,是急需解决的问题。文章针对数据挖掘技术及其在高校后勤中的应用展开讨论和研究。     

New aspects of risk aversion some applications in portfolio theory

In the paper the demand for a risky asset is considered as a function of additional random effects on wealth. For special structures of stochastic dependency between the rate of return of the risky asset and additional random wealth it is demonstrated that expected utility theory is a too inflexible instrument to analyze these effects. Expected local utility theory is shown to be more flexible. In case of independency absolute risk aversion is a proper criterion to give intuitive results in this case, too.

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Sunto La domanda di un titolo aleatorio è assunta come funzione di effetti aleatori addizionali sulla ricchezza.Per tipi speciali di dipendenza stocastica tra tasso di rendimento del titolo aleatorio e effetti aleatori addizionali si dimostra che la teoria dell'utilità attesa è troppo rigida come strumento di analisi efficace.Si mostra la maggior flessibilità della teoria dell'utilità attesa locale. Inoltre, in caso di indipendenza, l'avversione al rischio assoluta è un criterio adatto per dare risultati intuitivi.

     

Asymptotic normality of M-estimators in a semiparametric model with longitudinal data

Suppose that the longitudinal observations (Y _ij , X _ij , t _ij ) for i = 1, . . . ,n; j = 1, . . . ,m _i are modeled by the semiparamtric model where β ₀ is a k × 1 vector of unknown parameters, g(·) is an unknown estimated function and e _ij are unobserved disturbances. This article consider M-type regressions which include mean, median and quantile regressions. The M-estimator of the slope parameter β ₀ is obtained through piecewise local polynomial approximation of the nonparametric component. The local M-estimator of g(·) is also obtained by replacing β ₀ in model with its M-estimator and using local linear approximation. The asymptotic distribution of the estimator of β ₀ is derived. The asymptotic distributions of the local M-estimators of g(·) at both interior and boundary points are also established. Various applications of our main results are given. The research is supported in part by National Natural Science Foundation of China (Grant No. 10671089).     

Soft landings: Difficult but by no means impossible

• ? Policymakers have rarely engineered soft landings, but central banks may be better placed to do so this time around. Crucially though, this relies on central banks being willing and able to use unconventional policy aggressively and China remaining the global shock‐absorber of last resort.
• ? The ability of policymakers to deliver a soft landing will depend on both cyclical and structural factors (see table below). Cyclical risks from prior over‐tightening by central banks or a burst of inflation limiting room to loosen policy seem small. The main cyclical worries currently stem from major financial market sell‐offs and balance sheet problems – and while these risks have risen, they are less of a worry than in 2007.
• ? On the structural side, less volatile activity and better data should make it quicker and easier to identify shocks than in prior decades. Meanwhile, transparent monetary policy frameworks and anchored inflation are limiting fears of inflation overshooting from constraining policy action. But some structural changes may hinder: globalisation has led to freer capital flows, which have the capacity to be destabilising and add to volatility.
• ? There are two other key uncertainties that will determine the likelihood of a gentle deceleration in growth. The first is the degree to which central banks are prepared to aggressively use unconventional policy and the effectiveness of this at delivering a boost to GDP growth. The second will be China's willingness to take growth‐boosting action. Even though Chinese policymakers may put a floor under growth, there is less chance than in the past of a large surge in imports.

     

Specification and testing of some modified count data models

This paper explores the specification and testing of some modified count data models. These alternatives permit more flexible specification of the data-generating process (dgp) than do familiar count data models (e.g., the Poisson), and provide a natural means for modeling data that are over- or underdispersed by the standards of the basic models. In the cases considered, the familiar forms of the distributions result as parameter-restricted versions of the proposed modified distributions. Accordingly, score tests of the restrictions that use only the easily-computed ML estimates of the standard models are proposed. The tests proposed by Hausman (1978) and White (1982) are also considered. The tests are then applied to count data models estimated using survey microdata on beverage consumption.     

Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts

We propose a family of regression models to adjust for nonrandom dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relatively to the parametric model at times where the dropout rate is high or the dropout model is misspecified. The methodology's practical utility is illustrated in a data analysis.