SUMMARY The sea-level is the composition of astronomical tidal and meteorological surge processes. It exhibits temporal non-stationarity due to a combination of long-term trend in the mean level, the deterministic tidal component, surge seasonality and interactions between the tide and surge. We assess the effect of these non-stationarities on the estimation of the distribution of extreme sea-levels. This is important for coastal flood assessment as the traditional method of analysis assumes that, once the trend has been removed, extreme sea-levels are from a stationary sequence. We compare the traditional approach with a recently proposed alternative that incorporates the knowledge of the tidal component and its associated interactions, by applying them to 22 UK data sites and through a simulation study. Our main finding is that if the tidal non-stationarity is ignored then a substantial underestimation of extreme sea-levels results for most sites. In contrast, if surge seasonality and the tide–surge interaction are not modelled the traditional approach produces little additional bias. The alternative method is found to perform well but requires substantially more statistical modelling and better data quality.

Stimulation of hepatocyte proliferation by epidermal growth factor (EGF) and insulin is inhibited by transforming growth factor beta (TGF-beta) and by glucagon. It is also suppressed by inhibitors of various protein kinases, including rapamycin, which blocks activation of p70 S6 kinase (p70(S6k)), PD98059, which inhibits the activation of extracellular-regulated kinase (ERK), and SB 203580, an inhibitor of the p38 mitogen-activated protein kinase (p38 MAPK). In this study, we investigated whether the inhibition of proliferation by TGF-beta involves these protein kinase cascades. Culture of hepatocytes with TGF-beta for 16 hours decreased the stimulation by EGF of ERK2 and p70(S6k) (by 50% and 35%, respectively), but did not affect the stimulation of either p38 MAPK, c-jun NH2-terminal kinase (JNK), or protein kinase B (PKB). Culture of hepatocytes with glucagon for 16 hours also inhibited the stimulation by EGF of activation of ERK2 and p70(S6k) (by approximately 50%). The inhibitory effects of glucagon were observed when the hormone was added either 10 minutes or 60 minutes before EGF addition, whereas no effects of TGF-beta were observed after 10-minute or 60-minute incubation. These results suggest that the inhibition of hepatocyte proliferation by TGF-beta may be in part mediated by inhibition of ERK2 and p70(S6k), but does not involve PKB, JNK, or p38 MAPK. Unlike glucagon, the effects of TGF-beta are not elicited in response to short-term treatment.

Estimation of the extremal behavior of a process is often based on the fitting of asymptotic extreme value models to relatively short series of data. Maximum likelihood has emerged as a flexible and powerful modeling tool in such applications, but its performance with small samples has been shown to be poor relative to an alternative fitting procedure based on probability weighted moments. We argue here that the small-sample superiority of the probability weighted moments estimator is due to the assumption of a restricted parameter space, corresponding to finite population moments. To incorporate similar information in a likelihood-based analysis, we propose a penalized maximum likelihood estimator that retains the modeling flexibility and large-sample optimality of the maximum likelihood estimator, but improves on its small-sample properties. The properties of the penalized likelihood estimator are verified in a simulation study, and in application to sea-level data, which also enables the procedure to be evaluated in the context of structural models for extremes.

The problem of estimating the probability of extreme sea-levels along a coastline has received little attention. Most of the existing analyses are univariate approaches that are applied independently to data from individual sites. We present a spatial extension of the methods that integrates information from the data sites and exploits knowledge of the spatial variation of the tidal and surge constituents of the sealevel along a coastline, to produce estimates at any coastal location. We illustrate the method by application to the UK east coast providing a set of design level estimates along the entire coastline. (C) 1998 John Wiley gr Sons, Ltd.

We consider the problem of small sample inference for the generalised extreme value distribution. In particular, we show the existence of approximate and exact ancillary statistics for this distribution and that small sample likelihood based inference is greatly improved by conditioning on these statistics. Ignoring the ancillary statistics in inference can have severe consequences in some standard applications of extreme value theory. We illustrate this via simulation and by analysis of two data sets, one based on sea-levels and the other on insurance claims.

Threshold methods for multivariate extreme values are based on the use of asymptotically justified approximations of both the marginal distributions and the dependence structure in the joint tail. Models derived from these approximations are fitted to a region of the observed joint tail which is determined by suitably chosen high thresholds. A drawback of the existing methods is the necessity for the same thresholds to be taken for the convergence of both marginal and dependence aspects, which can result in inefficient estimation. In this paper an extension of the existing models, which removes this constraint, is proposed. The resulting model is semi-parametric and requires computationally intensive techniques for likelihood evaluation. The methods are illustrated using a coastal engineering application.

The majority of studies of long-term sea-level change have concentrated on the trend in mean sea-level which is just one constituent of the trend in extreme sea-level. By fitting a spatial model to sea-level annual maxima from 62 UK sites, extreme sea-level trend estimates are obtained for the entire British coastline. These estimates exhibit smooth, but significant, spatial variation which arises from a combination of eustatic extreme sea-level change and local vertical land movements. Once the latter effect is removed, eustatic extreme sea-level trends are found to have no significant spatial variation and to be similar in value to trends in UK eustatic mean sea-level.