Quasi-likelihood inference in a generalized linear mixed model for balanced data

For modelling the effect of crossed, fixed factors on the response variable in balanced designs with nested stratifications, a generalized linear mixed model is proposed. This model is based on a set of quasi-likelihood assumptions which imply quadratic variance functions. From these variance functions, deviances are obtained to quantify the variation per stratification. The effects of the fixed factors will be tested, an dispersion components will be estimated. The practical use of the model is illustrated by reanalysing a soldering failures problem.     

Optimal transformations for categorical autoregressive time series

This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze categorical time series. The method uses an alternating least squares algorithm that consists of two main steps: estimation and transformation. Nominal, ordinal and numerical data can be analyzed. Some alternative applications of the general idea are highlighted: intervention analysis, smoothing categorical time series, predictable components, spatial modeling and cross-sectional multivariate analysis. Limitations, modeling issues and possible extensions are briefly indicated.     

Estimating structural equation models with non-normal variables by using transformations

We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution-free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non-normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.     

Simple alternatives for Box–Cox transformations

Simple transformations are given for reducing/stabilizing bias, skewness and kurtosis, including the first such transformations for kurtosis. The transformations are based on cumulant expansions and the effect of transformations on their main coefficients. The proposed transformations are compared to the most traditional Box–Cox transformations. They are shown to be more efficient.     

Joint estimation and testing for functional form and heteroskedasticity

This paper develops the maximum likelihood procedure to estimate the appropriate functional form in regression models with heteroskedastic errors. The analysis can then enable us to separate out the influence of non-linearity in an estimate of the transformation parameter from the influence of stabilizing the error variance. Illustrative examples have been used to show that estimation and tests for functional form and heteroskedasticity can and should be jointly considered.     

不同基准下坐标转换的方法及注意事项

不同基准的坐标转换,如果其转换范围较大,都是在空间直角坐标系下进行的;如果其坐标的表示形式是大地坐标或高斯平面坐标,则需要首先将其转换成空间直角坐标,然后才能依据转换参数及模型进行转换。本文以一个实例,叙述了不同基准坐标转换的具体过程。     

Properties of nonlinear transformations of fractionally integrated processes

This paper shows that the properties of nonlinear transformations of a fractionally integrated process strongly depend on whether the initial series is stationary or not. Transforming a stationary Gaussian I(d) process with d>0 leads to a long-memory process with the same or a smaller long-memory parameter depending on the Hermite rank of the transformation. Any nonlinear transformation of an antipersistent Gaussian I(d) process is I(0)). For non-stationary I(d) processes, every polynomial transformation is non-stationary and exhibits a stochastic trend in mean and in variance. In particular, the square of a non-stationary Gaussian I(d) process still has long memory with parameter d, whereas the square of a stationary Gaussian I(d) process shows less dependence than the initial process. Simulation results for other transformations are also discussed.     

Human and organizational capital: typologies among Spanish firms

This paper uses survey data on 965 Spanish manufacturing firms to examine the implementation of innovative management practices and the relationship of this with the organization of work and human resource management. The paper takes into account transformations in technology, quality management and the organization of work. Using cluster analysis, we identify the different paths that firms are following in order to improve their performance, finding that simultaneous transformations in several dimensions lead to greater success than partial transformation, or none at all.     

Estimating the variance of a finite population under a superpopulation model

The problem of deriving optimum sampling strategies for estimating the variance of finite population has been considered under some super-population set up.     

Nonparametric inference on multivariate versions of Blomqvist’s beta and related measures of tail dependence

We consider nonparametric estimation of multivariate versions of Blomqvist’s beta, also known as the medial correlation coefficient. For a two-dimensional population, the sample version of Blomqvist’s beta describes the proportion of data which fall into the first or third quadrant of a two-way contingency table with cutting points being the sample medians. Asymptotic normality and strong consistency of the estimators are established by means of the empirical copula process, imposing weak conditions on the copula. Though the asymptotic variance takes a complicated form, we are able to derive explicit formulas for large families of copulas. For the copulas of elliptically contoured distributions we obtain a variance stabilizing transformation which is similar to Fisher’s z-transformation. This allows for an explicit construction of asymptotic confidence bands used for hypothesis testing and eases the analysis of asymptotic efficiency. The computational complexity of estimating Blomqvist’s beta corresponds to the sample size n, which is lower than the complexity of most competing dependence measures.      

Neoliberalization of Istanbul's Nightlife: Beer or Champagne?

This article shows how the transformation of Istanbul's entertainment industry and of Beyo?lu, Istanbul's oldest, largest and the most diverse entertainment district, represents and reproduces spatial and economic divisions in the city. We argue that these differences also become compounded and intertwined with distinctions in consumption and taste. Taking a simultaneous look at the spatial, economic and symbolic transformations of the entertainment industry enables us to understand how and why these intense divisions emerge, and what kind of contestations, rationalizations and resistance strategies are at work in this transformation. A major contribution of this article is to document and discuss the political economy of the process of urban transformation in the city through the lens of the entertainment industry, providing an interesting case of ‘neoliberalism on the ground'. Examining the neoliberalization of nightlife in a relatively understudied context, Istanbul, also reveals that its segmentation and spatial inequality are not just determined by political economy but are also constitutive of it. By adding the concept of ‘image consumption' and taste distinctions into the analysis, the article also uncovers the symbolic nature of the ongoing transformations. Finally, exploring Beyo?lu as a district in transition with persistent contestations contributes, in turn, to the right to the city debate.     

Toward Capitalism or Away from Russia?: Early Stage of Post‐Soviet Economic Reforms in Belarus and the Baltics

While at the start of systemic economic transformations in Eastern Europe, both indigenous reformers and Western observers tended to interpret the events as a rationally conceived and executed replacement of an economic system of inferior performance by another one whose superiority was proven, the developments that followed the disintegration of the Soviet polity did not support this view. Shortly after the former Soviet republics gained independence, they exhibited marked variations in speed and sometimes direction of market transformation that could not be satisfactorily explained within the framework of economic theories used by promoters of reforms. In this paper I compare the early stages of economic transformation in Belarus, where reforms were eventually abandoned, with the three Baltic states (Latvia, Lithuania, Estonia), which are the most successful emerging market economies among the former Soviet republics. In this comparison I will attempt to highlight non-economic factors that might have contributed to this difference.     

Optimum allocation in stratified sampling with replacement

In a stratified sample, when sampling is done with replacement in each stratum a better estimate of the population mean can be achieved by considering the distinct units only. An explicit expression for the variance for the mean, of a stratified sample based on the distinct units only, is obtained. Then the optimum allocation for the different stratum are obtained by minimizing this variance subject to (i) total sample size being fixed, or (ii) the expected number of distinct units being fixed. Neyman’s solutions are obtained as special cases. The solutions finally arrived at are algebraically complex, hence, numerical methods are applied. In all examples, the variance of the estimates obtained by this method are smaller than the variances obtained by Neyman’s allocation. A part of this work was supported by the Office of the Ordinance Research; U.S.A. Grant (DA-AROL(D)-31-124-G83) when the author was at University of California, Berkeley.     

Properties of optimal forecasts under asymmetric loss and nonlinearity

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of optimal forecasts can be invalid under asymmetric loss and nonlinear data generating processes and thus may be very misleading as a benchmark for an optimal forecast. We establish instead that a suitable transformation of the forecast error—known as the generalized forecast error—possesses an equivalent set of properties. The paper also provides empirical examples to illustrate the significance in practice of asymmetric loss and nonlinearities and discusses the effect of parameter estimation error on optimal forecasts.     

Changing the conversation on evaluating digital transformation in healthcare: Insights from an institutional analysis

The global health sector is engaged in significant digital transformation. Evaluating these transformations is important given the major investments involved and their major potential consequences. However, studies have critiqued both the quality of evaluations and the quality of evaluation research. The persistent lack of progress has led researchers to ask deeper questions about what is actually occurring when teams evaluate the benefits of digital transformation. This translational research essay explores how institutional theory offers a lens for understanding the complexities of evaluating digital transformations in healthcare and provides insights for improving it. In particular, we show how institutional theory can explain behaviors observed in the literature and in our own case study. We also show how institutional theory can benefit from the insights observed in evaluation work. Motivated by these opportunities, we suggest an agenda through which practitioners and researchers can jointly improve work in this area.     

The incompatibility of achieving a fully specified linear model with assuming that residual dependent-variable variance is random

The residual dependent-variable variance in experiments is not “random error”, as it is often assumed to be, but merely “unaccounted for variance”, because what is random is inexplicable in terms of any possible set of independent-variables and this is something that ultimately is only empirically determinable. So, if there is any unaccounted for dependent-variable variance, an experiment’s set of independent-variables is certainly under-specified and perhaps mis-specified because of the confounding of variables included in this set by causally relevant variables not included in the set. Thus, the proper first empirical test of any linear model is whether it leaves any residual dependent-variable variance, and if it does then none of its independent variables can yet logically justifiably be claimed to predict or causally explain any of the dependent-variable variance whatsoever.     

Testing the nominal-to-real transformation

This paper establishes the necessary and sufficient condition for nominal-to-real data transformations routinely used in empirical work to reduce the order of integration of an I(2) vector process while retaining the cointegrating relations among the variables. The condition potentially fails in a direction which is often dealt with by assumption in applied work. In this case, the transformed process satisfies a well-specified vector equilibrium model, yet I(1) inference and interpretation based on the real transformed system are invalidated. An easy-to-implement sequential test of the transformation based on I(1) cointegration methods demonstrates good size and power properties. An empirical example illustrates the need to test the nominal-to-real transformation.     

Box–Cox transforms for realized volatility

The log transformation of realized volatility is often preferred to the raw version of realized volatility because of its superior finite sample properties. One of the possible explanations for this finding is the fact the skewness of the log transformed statistic is smaller than that of the raw statistic. Simulation evidence presented here shows that this is the case. It also shows that the log transform does not completely eliminate skewness in finite samples. This suggests that there may exist other nonlinear transformations that are more effective at reducing the finite sample skewness.     

企业内部知识转化模型分析

企业内部知识转化是企业知识创新的关键。由于不同层次主体组织形式不同,其知识结构和情境特征也存在差异,使企业内部知识层次转化具有动态复杂性。本文通过对个体、团队和企业组织知识特征的分析,提出了企业内部知识转化存在同一层次内部共享和跨层次跃迁两个部分,且企业内部知识的前馈和反馈转化不受时空限制。从知识竞争过程分析,不同主体的知识转化均包括知识的消化吸收和转化规则生成两个过程。     

Characterization of optima in smooth Pareto economic systems

Simple techniques of calculus and geometry are used to study and characterize the optima of pure exchange economies in which the utility functions are smooth but not necessarily convex. It is also shown how one can reduce the problem of optimizing p functions on the manifold of states to that of maximizing a single function on a submanifold of this space. Two models are described: one in which a person cannot trade to an optimum unless he starts at one; and one in which a person cannot even get near a local Pareto optimum along continuous ‘trade curves’ from most initial distributions. Finally, the set of optima is described for a generic set of utility mappings.