A time series approach to life table construction

This paper deals with the construction of the life table. A discussion of basic facts about the life table is followed by the proposal of a nonstationary, autoregressive model for the life table. The moment structure of the nonstationary, autoregressive model is developed. Some estimation procedures are proposed followed by several examples.     

Effect of systematic sampling on arima models

Given a general homogeneous non-stationary autoregressive integrated moving average process ARIMA(p,d,q), the corresponding model for the subseries obtained by a systematic sampling is derived. The article then shows that the sampled subseries approaches approximately to an integrated moving average process IMA(d,l), l≤(d-l), regardless of the autoregressive and moving average structures in the original series. In particular, the sampled subseries from an ARIMA (p,l,q) process approaches approximately to a simple random walk model.     

The effects of correlation among observations on the consistency property of sample variance

It is well known that even when the sample observations are correlated and not normal the sample variance, S² converges in probability to E(S²). But the required sample size for S² to be a consistent estimator of E(S²) is an open question. Some light is shed on this question in this paper. In particular the relation between the rate of convergence and the correlation property of the observations is explored. It is shown that the retardation to the rate of convergence is not appreciable if the correlation is moderate but it can be severe for extreme correlations.     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.     

Control Chart for Monitoring Autocorrelated Process with Multiple Exogenous Inputs

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes.     

Strategy as engagement: What organization strategy can learn from military strategy

Strategy process and practice research has illuminated the internal dynamics of strategy work – at the cost of backgrounding processes and practices that relate to engagement with external actors. In this conceptual paper, we argue for an extension of this body of work by shifting the focus of research from internal practices and processes towards externally oriented practices of engagement. We do so by critically building on the military strategy literature and develop the concept of strategy as engagement. This concept suggests understanding the role of strategy as bridge between policy and tactics; the importance of grand strategy as the making of policy; and the need to focus attention on tactics as distributed collective action. Thus, we contribute to strategy process and practice research through 1) extending its repertoire to practices of engagement and 2) broadening its epistemic foundation through a critical reading of military strategy.     

From a dyadic to a triadic perspective: Divestiture research implications for understanding pre- and post-acquisition processes

Acquisition and divestiture studies remain surprisingly disconnected, although both fields address corporate restructuring modes that are tightly linked. Acquisitions entail buying a business that another company divests or even, in many cases, a subsequent sale of formerly acquired activities. In this paper, we adopt a seller perspective, explore cross-fertilization areas, and identify important complementary and challenging insights that divesture research can bring to acquisition research. Focusing on sell-off, a major divesture type that is considered symmetrical to an acquisition, we discuss how divestiture research can shed novel light on pre- and post-acquisition stakeholders and processes. We propose two ways in which a divestiture perspective invites rethinking: 1) regarding actors, by stressing the need to consider triadic (divester–target–acquirer) rather than dyadic configurations (target–acquirer) and 2) regarding processes and time, by enlarging the temporal horizon of acquisition processes by divestiture processes, which precede but also sometimes exceed the acquisition. We contribute to acquisition research by providing a fresh look at stakeholder and process perspectives and identifying important future research avenues.     

Max‐infinitely divisible models and inference for spatial extremes

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max‐stable models are inadequate to capture the rate of joint tail decay, and to estimate joint extremal probabilities beyond observed levels. We here develop a more flexible modeling framework based on the class of max‐infinitely divisible processes, which extend max‐stable processes while retaining dependence properties that are natural for maxima. We propose two parametric constructions for max‐infinitely divisible models, which relax the max‐stability property but remain close to some popular max‐stable models obtained as special cases. The first model considers maxima over a finite, random number of independent observations, while the second model generalizes the spectral representation of max‐stable processes. Inference is performed using a pairwise likelihood. We illustrate the benefits of our new modeling framework on Dutch wind gust maxima calculated over different time units. Results strongly suggest that our proposed models outperform other natural models, such as the Student‐t copula process and its max‐stable limit, even for large block sizes.     

Infill asymptotics for adaptive kernel estimators of spatial intensity

We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.