Number Of Common Factors Research Articles

Lessons learnt while integrating services for children: qualitative interviews with professional stakeholders

BackgroundIn the English NHS, integrated care is seen as an opportunity to deliver joined-up care for children and families. This paper examines the lessons learnt by professional stakeholders in the process of developing different examples of integrated models of care/frameworks for children’s services.MethodsInitial desk research was undertaken to identify different examples of integrated care models and systems/frameworks for children’s services. This identified forty-three examples in England. Of these, twelve examples were shortlisted after consultation with the senior managers within the Health and Care Partnership that had commissioned the research, and a more detailed online search for published documents was undertaken. Semi-structured qualitative interviews were then conducted with sixteen professional stakeholders in eight of these examples, ranging from one to four interviewees per example. Interviews focused on the lessons learnt from integrating and transforming services. Data were analysed using framework analysis.ResultsThe eight examples vary in their design but have several broad commonalities. A number of common themes and learning have emerged, of which two were identified within all eight examples: the first is about focusing on children and young people; the second is about focusing on partner engagement and collaboration and the importance of building trust and relationships between partners. A number of other important themes also emerged together with several challenges.ConclusionsA number of common factors were identified that are essential to success in integrating health and care systems. Common across all localities were being child-centric and focusing on child outcomes plus the importance of building trust, engagement and relationships with partners. The findings can help health and care system leaders transform services to ensure efficiency, improvement in services and integration.

A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data

This article studies a high-dimensional factor model with sparse idiosyncratic covariance matrix in continuous time, using asynchronous high-frequency financial data contaminated by microstructure noise. We focus on consistent estimations of the number of common factors, the integrated covariance matrix and its inverse, based on the flat-top realized kernels introduced by Varneskov. Simulation results illustrate the satisfactory performance of our estimators in finite samples. We apply our methodology to the high-frequency price data on a large number of stocks traded in Shanghai and Shenzhen stock exchanges, and demonstrate its value for capturing time-varying covariations and portfolio allocation.

Modeling High-Dimensional Time Series: A Factor Model With Dynamically Dependent Factors and Diverging Eigenvalues

This article proposes a new approach to modeling high-dimensional time series by treating a p-dimensional time series as a nonsingular linear transformation of certain common factors and idiosyncratic components. Unlike the approximate factor models, we assume that the factors capture all the nontrivial dynamics of the data, but the cross-sectional dependence may be explained by both the factors and the idiosyncratic components. Under the proposed model, (a) the factor process is dynamically dependent and the idiosyncratic component is a white noise process, and (b) the largest eigenvalues of the covariance matrix of the idiosyncratic components may diverge to infinity as the dimension p increases. We propose a white noise testing procedure for high-dimensional time series to determine the number of white noise components and, hence, the number of common factors, and introduce a projected principal component analysis (PCA) to eliminate the diverging effect of the idiosyncratic noises. Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n increases to infinity. We use both simulated data and real examples to assess the performance of the proposed method. We also compare our method with two commonly used methods in the literature concerning the forecastability of the extracted factors and find that the proposed approach not only provides interpretable results, but also performs well in out-of-sample forecasting. Supplementary materials for this article are available online.

Sequential testing for structural stability in approximate factor models

We develop a monitoring procedure to detect changes in a large approximate factor model. Letting r be the number of common factors, we base our statistics on the fact that the r+1-th eigenvalue of the sample covariance matrix is bounded under the null of no change, whereas it becomes spiked under changes. Given that sample eigenvalues cannot be estimated consistently under the null, we randomise the test statistic, obtaining a sequence of i.i.d. statistics, which are used for the monitoring scheme. Numerical evidence shows a very small probability of false detections, and tight detection times of change-points.

A multi-step procedure to determine the number of factors in large approximate factor models

We propose in this article a multi-step procedure to determine the number of common factors in large approximate factor models (Chamberlain and Rothschild 1983) in the spirit of the eigenvalue ratio (ER) criterion of Ahn and Horenstein (2013). We show theoretically that this multi-step procedure can consistently identify the true number of factors while both relatively strong and weak factors simultaneously exist, which is not easy to perform with the ER criterion. Our extensive simulation results demonstrate that the proposed procedure has better finite sample properties compared with that of the ER criterion under certain cases.

Dental Functionality: construction and validation of an oral health indicator for institutionalized elderly persons in the city of Natal, Rio Grande do Norte

Abstract Objective: to suggest a composite indicator that identifies the oral health condition of institutionalized elderly persons. Method: an observational and cross-sectional study was performed. A total of 315 elderly persons were investigated in long-stay care facilities for the elderly in the city of Natal, Rio Grande do Norte, Brazil. Such individuals underwent an epidemiological evaluation of their oral health conditions, based on the DMFT index, CPI and the PAL (periodontal attachment loss) index. Factor analysis was used to identify a relatively small number of common factors by principal component analysis. Results: five oral health variables were included in factor analysis, and using the Kaiser criterion, which considers the percentage of variance explained by the factors, a single factor which together explained 79.7% of the total variance of the variables included in the analysis model was selected. This factor was analyzed and interpreted according to the dimension to which it related, and was entitled the factor of Dental Functionality. Conclusion: this factor generated an objective indicator to characterize the oral health of the elderly in long-term care facilities for the elderly of Natal, Rio Grande do Norte, and represents a parameter for studies of the oral health outcomes of this elderly population. It also revealed a change in the dental profile of this population with more teeth present in the mouth and a reduction in edentulism.

Premature immunosenescence impairs immune surveillance allowing the endometriotic stemcell to migrate: The cytokine profile as a common denominator

While endometriosis, one of the most common reasons for infertility, remains a multifactorial condition and its exact cause highly speculative, there are data pointing to novel pathways of disease initiation which involve a stem cell and its ability to migrate and implant after it differentiates into an endometriotic stem cell. Thus, the mechanisms conferring immune surveillance, which would also normally expel the mesenchymal endometriotic cell, impairing its migration and implantation, appear to be negatively influenced by a state of endometriotic premature immunosenescence. This interplay between the two immunological mechanisms and endometriosis is influenced by a number of common factors having an active role in the host's protection process that inhibits harmful diseases and maintains cellular homeostasis. It appears more than coincidental that production/inhibition of IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-15, IL-18, TNF-α, VEGF, ICAM-1, and the number of Tolllike receptors is the same in ...

요인 개수 결정을 위한 평행분석의 정확성 평가

요인 개수 결정을 위한 평행분석의 정확성 평가

On the number of common factors with high-frequency data

SummaryIn this paper, we introduce a local principal component analysis approach to determining the number of common factors of a continuous-time factor model with time-varying factor loadings using high-frequency data. The model is approximated locally on shrinking blocks using discrete-time factor models. The number of common factors is estimated by minimizing the penalized aggregated mean squared residual error over all shrinking blocks. While the local mean squared residual error on each block converges at rate $\min(n^{1/4}, p)$, where $n$ is the sample size and $p$ is the dimension, the aggregated mean squared residual error converges at rate $\min(n^{1/2}, p)$; this achieves the convergence rate of the penalized criterion function of the global principal component analysis method, assuming restrictive constant factor loading. An estimator of the number of factors based on the local principal component analysis is consistent. Simulation results justify the performance of our estimator. A real financial dataset is analysed.

A Central Limit Theorem for the Number of Factors with High Frequency Data

In the literature, consistency of the estimates of the number of factors for both the discrete and continuous time factor models has been extensively studied recently. But the central limit theorem has long been unsolved. In this paper, alternative to the PCA-based approach, we construct a new estimator of the number of common factors of the continuous time factor model using large panel high-frequency data contaminated with noise. We establish the central limit theorem of the new estimator and then apply it to the statistical inference on the number of common factors. Compared with the PCA-based estimators in the existing literatures, ours is proved to have asymptotic normal distribution and robust to the presence of the microstructure noise.

Testing for Common Factors in Large Factor Models

We consider large factor models with unobserved factors. We formalize the notion of common factors between different groups of variables and propose to use it as a general approach to study the structure of factors, i.e., which factors drive which variables. The spanning hypothesis, which states that factors driving one group are spanned by those driving another group, can be studied as a special case under our framework. We develop a statistical procedure for testing the number of common factors. Our inference procedure is built upon recent results on high-dimensional bootstrap and is shown to be valid under the asymptotic framework of large n and large T. In Monte Carlo simulations, our procedure performs well in finite samples. As an empirical application, we construct confidence sets for the number of common factors between the macroeconomy and the financial markets.

Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency

ABSTRACTThis article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.

The Origin of New-Onset Diabetes After Liver Transplantation: Liver, Islets, or Gut?

New-onset diabetes is a frequent complication after solid organ transplantation. Although a number of common factors are associated with the disease, including recipient age, body mass index, hepatitis C infection, and use of immunosuppressive drugs, new-onset diabetes after liver transplantation (NODALT) has the following unique aspects and thus needs to be considered its own entity. First, a liver graft becomes the patient's primary metabolic regulator after liver transplantation, but this would not be the case for kidney or other grafts. The metabolic states, as well as the genetics of the graft, play crucial roles in the development of NODALT. Second, dysfunction of the islets of Langerhans is common in cirrhotic patients and would be exacerbated by immunosuppressive agents, particularly calcineurin inhibitors. On the other hand, minimized immunosuppressive protocols have been widely advocated in liver transplantation because of liver tolerance (immune privilege). Third and last, through the "gut-liver axis," graft function is closely linked to gut microbiota, which is now considered an important metabolic organ and known to independently influence the host's metabolic homeostasis. Liver transplant recipients present with specific gut microbiota that may be prone to trigger metabolic disorders. In this review, we proposed 3 possible sites for the origin of NODALT, which are liver, islets, and gut, to help elucidate the underlying mechanism of NODALT.

Factor analysis of fatal road traffic crashes with massive casualties in China

Factor analysis refers to a collection of statistical methods for reducing correlational data into a smaller number of dimensions or factors. In this study, factor analysis theory was used to determine the main influential factors of road traffic crashes with massive casualties. Twenty variables related to personnel, vehicles, roads, and environment were collected, and the significance of their correlations was tested for validity. A correlation coefficient matrix R was calculated, and its latent root [Formula: see text] was obtained based on the characteristic equation. A number of common factors were determined according to the value of latent root [Formula: see text]. Factor loading was used to express the relationship of each variable to the underlying main influential factors. An index system of accident factors was developed based on the results of factor loading, and the weight of each factor was calculated to classify the factor influence. The main influential factors of accidents were determined to be fault behavior, driving experience, condition of vehicle safety, purpose of vehicle, road lighting, driver, road surface condition, roadside protection facilities, and road terrain.

Identification of Common Factors in Multivariate Time Series Modeling

For multivariate time series modelling, it is essential to know the number of common factors that define the behaviour. The traditional approach to this problem is investigating the number of cointegration relations among the data by determining the trace and the maximum eigenvalue and obtaining the number of stationary long-run relations. Alternatively, this problem can be analyzed using dynamic factor models, which involves estimating the number of common factors, both stationary and not, that describe the behaviour of the data. In this context, we empirically analyze the power of such alternative approaches by applying them to time series that are simulated using known factorial models and to financial market data. The results show that when there are stationary common factors, when the number of observations is reduced and/or when the variables are part of more than one cointegration relation, the common factors test is more powerful than the usually applied cointegration tests. These results, together with the greater flexibility to identify the loading matrix of the data generating process, render dynamic factor models more suitable for use in multivariate time series analysis.

Interim results in clinical trials: Do we need to keep all interim randomised clinical trial results confidential?

ObjectivesGuidelines for the conduct of clinical trials emphasize the importance of keeping the interim results from the main endpoints confidential, in order to maintain the integrity of the trial and to safeguard patients’ interests. However, is this essential in every situation? Materials and methodsWe review the evidence for these guidelines and consider recent randomised trials that have released interim results, to assess their impact on the success of the trial. However, because the strength of opinion to keep interim results confidential is so strong, there are limited examples of such trials. ResultsIn the QUARTZ trial (which is assessing the value of whole brain radiotherapy in patients with brain metastases from non-small cell lung cancer) the decision to release interim results was taken in response to threatened closure due to poor accrual, whereas in the GRIT trial (which compared two obstetric strategies for the delivery of growth retarded pre-term fetuses) the regular release of interim results was pre-planned. Nevertheless there are a number of common factors between these two trials. In particular, the trial treatments were already in wide use, with no reliable randomised evidence on which treatment should be used for which patients, and there was diverse clinical opinion, which meant that accrual was likely to be challenging. In a situation where a quarter to a third of trials do not accrue their required number of patients, the QUARTZ trial continues to accrue patients, and the GRIT trial successfully accrued its target of nearly 600 babies. ConclusionsThis article therefore argues that there is a need to re-consider whether it is always essential to keep the interim results of randomized clinical trials confidential, and suggests some criteria that may help groups planning or running challenging trials decide whether releasing interim results would be a useful strategy.

Common-use check-in counter reassignments with a variable number of service lines and variable length of time window

In the real world, there are a number of common factors that can cause perturbations at check-in counters, such as an airport closure, a temporary power failure, crashing of the counter computer, baggage conveyor belt malfunctions, and so on, forcing the airport authority to reassign flights to new check-in counters. In this study, we develop a model and a solution method to solve common-use check-in counter reassignment problems with a variable number of service lines and a variable length of time window. To test how well the proposed model and the solution method can be applied to the real world, we perform numerical tests based on a hypothetical incident in relation to a Taiwan airport’s operations. The test results show that the model and solution would be useful for the airport authority in actual operations.

The Politics of What Works in Service Provision: An Evidence-Based Review

This paper examines the evidence on the forms of politics likely to promote inclusive social provisioning and enable, as opposed to constrain, improvements in service outcomes. It focuses on eight relatively successful cases of delivery in a range of country contexts and sectors (roads, agriculture, health, education) where independent evaluations demonstrate improved outcomes. The paper traces the main characteristics of the political environment for these cases, from the national political context, to the politics of sector policymaking, to the micro politics of implementation. The findings indicate that it is possible to identify connections between good performance and better outcomes at the point of delivery and the main forms of politics operating at local, sector and national levels. A number of common factors underpinning successful delivery emerge strongly but need to be tested through further research. In particular, the paper highlights the relationship between inclusive delivery and: -- periods of crisis and transition; -- the nature of the political settlement; -- the types of calculations of political returns being made by political actors at all levels, and; -- the extent to which the state derives or seeks to enhance its legitimacy through the provision of a particular service.

Determining the Rank of the Beta Matrix in Linear Asset Pricing Models

This paper pr oposesanew method to estimate the rank of the beta matrix in a factor model . We consider the case in which possible factor variables, which we call factor -candidate variables, are observed. The idiosyncratic error terms are allowed to be correlated bot h over different cross section units and over different time periods. For the factor model, estimating the rank of the beta matrix is equivalent to estimating the number of the relevant factors among the factor -candidate variables. The estimator we propo seis easy to use because it is computed with the eigenvalues of the inner product of an estimated beta matrix. Simulation results showthat the proposed method works well evenin small samples .Our analysis of US individual stock returns is consistent w ith the notion that the three factors of F ama and French(FF, 1993) capture three different risk sources. The five factors of Chen, Roll, and Ross (CRR, 1985) are correlated with one additional risk factor that is not related to the Fama-French factors.The momentum factors capture a further risksource that is not captured by the FF and CRR factors. In addition, the two factors proposed by Chen, Novy Marx and Zhang ( CNZ, 2010) capture a source of risk missed by all the above factors. These results sugg est that there are six common risk factors in US individual stock returns among the thirteen factor candidate used . Furthermore, our analysis of portfolio returns reveals that the estimated number of common risk sources changes depending on howthe portfoliosare constructed. The number of risk sources found from the analysis of portfolio returns is generally smaller than the number of common factors in the individual stock returns.

Generalized spatial dynamic factor models

This paper introduces a new class of spatio-temporal models for measurements belonging to the exponential family of distributions. In this new class, the spatial and temporal components are conditionally independently modeled via a latent factor analysis structure for the (canonical) transformation of the measurements mean function. The factor loadings matrix is responsible for modeling spatial variation, while the common factors are responsible for modeling the temporal variation. One of the main advantages of our model with spatially structured loadings is the possibility of detecting similar regions associated to distinct dynamic factors. We also show that the new class outperforms a large class of spatial-temporal models that are commonly used in the literature. Posterior inference for fixed parameters and dynamic latent factors is performed via a custom tailored Markov chain Monte Carlo scheme for multivariate dynamic systems that combines extended Kalman filter-based Metropolis–Hastings proposal densities with block-sampling schemes. Factor model uncertainty is also fully addressed by a reversible jump Markov chain Monte Carlo algorithm designed to learn about the number of common factors. Three applications, two based on synthetic Gamma and Bernoulli data and one based on real Bernoulli data, are presented in order to illustrate the flexibility and generality of the new class of models, as well as to discuss features of the proposed MCMC algorithm.