Jens Perch Nielsen

Sensitivity of monosynaptic test reflexes to facilitation and inhibition as a function of the test reflex size: a study in man and the cat

SummaryIn parallel experiments on humans and in the cat it was investigated how the sensitivity of monosynaptic test reflexes to facilitation and inhibition varies as a function of the size of the control test reflex itself. In man the monosynaptic reflex (the Hoffmann reflex) was evoked in either the soleus muscle (by stimulation of the tibial nerve) or the quadriceps muscle (by stimulation of the femoral nerve). In the decerebrate cat monosynaptic reflexes were recorded from the nerves to soleus and medial gastrocnemius muscles; they were evoked by stimulation of the proximal ends of the sectioned L7 and S1 dorsal roots. Various excitatory and inhibitory spinal reflex pathways were used for conditioning the test reflexes (e.g. monosynaptic Ia excitation, disynaptic reciprocal inhibition, cutaneous inhibition, recurrent inhibition, presynaptic inhibition of the Ia fibres mediating the test reflex). It was shown that the additional number of motoneurones recruited in a monosynaptic test reflex by a constant excitatory conditioning stimulus was very much dependent on the size of the test reflex itself. This dependency had the same characteristic pattern whatever the conditioning stimulus. With increasing size of the test reflex the number of additionally recruited motoneurones first increased, then reached a peak (or plateau) and finally decreased. A similar relation was also seen with inhibitory conditioning stimuli. The basic physiological factors responsible for these findings are discussed. Finally, the implications for the interpretation of experiments in man with the H-reflex technique are considered.

Methodological implications of the post activation depression of the soleus H-reflex in man

SummaryA long lasting inhibition (> 8 s) of the soleus Hoffmann reflex (H-reflex) was evoked by a preceding soleus H-reflex, by a brief voluntary ankle flexor or extensor muscle contraction or by a tap applied to the Achilles tendon. The time course of this long lasting inhibition was similar in all these cases, suggesting that the same spinal mechanism is involved. Furthermore, it was shown that the post-activation depression may interfere with the determination of inhibitory or facilitatory effects on the H-reflex. It is stressed that when the onset of inhibitory or facilitatory effects on the soleus H-reflex is to be determined in relation to start of an ankle movement, either very long stimulus intervals (> 8 s) must be used, or the onset must be determined in relation to a reference value of the soleus H-reflex, which may be influenced by the long lasting inhibitory effect, but not yet by the succeeding muscle contraction.

Kernel Density Estimation for Heavy-Tailed Distributions Using the Champernowne Transformation

When estimating loss distributions in insurance, large and small losses are usually split because it is difficult to find a simple parametric model that fits all claim sizes. This approach involves determining the threshold level between large and small losses. In this article, a unified approach to the estimation of loss distributions is presented. We propose an estimator obtained by transforming the data set with a modification of the Champernowne cdf and then estimating the density of the transformed data by use of the classical kernel density estimator. We investigate the asymptotic bias and variance of the proposed estimator. In a simulation study, the proposed method shows a good performance. We also present two applications dealing with claims costs in insurance.

Nonparametric autoregression with multiplicative volatility and additive mean

For over a decade, nonparametric modelling has been successfully applied to study nonlinear structures in financial time series. It is well known that the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag. When the mean has an additive structure, however, better estimation methods are available which fully exploit such a structure. Although in the past such nonparametric applications had been focused more on the estimation of the conditional mean, it is equally if not more important to measure the future risk of the series along with the mean. For the volatility function, i.e., the conditional variance given the past, a multiplicative structure is more appropriate than an additive one, as the volatility is a positive scale function and a multiplicative model provides a better interpretation of each lagged values influence on such a function. In this paper we consider the joint estimation of both the additive mean and the multiplicative volatility. The technique used is marginally integrated local polynomial estimation. The procedure is applied to the DEM/USD (Deutsche Mark/US Dollar) daily exchange returns.

Boundary and Bias Correction in Kernel Hazard Estimation

A new class of local linear azard estimators based on weig ted least square kernel estimation is considered. The class includes the kernel Hazard estimator of Ramlau-Hansen (1983), which as tHe same boundary correction property as the local linear regression estimator (see Fan and Gijbels, 1996). It is shown that all the local linear estimators in the class have the same pointwise asymp-totic properties. We derive the multiplicative bias correction of the local linear estimator. In addition we propose a new bias correction technique based on bootstrap estimation of additive bias. This latter method as nice theoretical properties. Based on an extensive simulation study where we compare the per-formance of competing estimators we also recommend the use of the additive bias correction in applied work.

Kernel density estimation of actuarial loss functions

Abstract In this paper we estimate actuarial loss functions based on a symmetrized version of the semiparametric transformation approach to kernel smoothing. We apply this method to an actuarial study of automobile claims. The method gives a good overall impression while estimating actuarial loss functions, since it is capable of estimating both the initial mode and the heavy tail that is so typical for actuarial and other economic loss distributions. We study the properties of the transformation kernel density estimation and show the differences with the multiplicative bias corrected estimator. We add insight into the kernel smoothing transformation method through an extensive simulation study with a particular view to the performance of the estimation at the tail.

Central facilitation of Ia inhibition during tonic ankle dorsiflexion revealed after blockade of peripheral feedback

SummaryRecent studies have reported that no increase of the disynaptic reciprocal inhibition can be observed during tonic voluntary dorsiflexion of the foot as compared to rest, when the size of the control H-reflex is kept constant. Other studies have, however, shown that a voluntary contraction evokes a strong and long-lasting depression of the synaptic transmission from Ia afferents to motoneurones, most likely secondary to activation of these afferents during the contraction (post-activation depression). It was thought that this effect could also interfere with the demonstration of a central facilitation of the reciprocal inhibition during movement. The amount of disynaptic Ia reciprocal inhibition from the pretibial flexors to the soleus H-reflex was therefore estimated in normal human subjects at rest and during voluntary tonic dorsiflexion before, during and after blocking the peripheral feedback from the investigated muscles. It was observed that the reciprocal inhibition measured during dorsiflexion increased during occlusion of the blood supply to the leg, reaching a maximum of inhibition after 30 min of ischaemia. After release of the ischaemia the inhibition gradually decreased to its pre-ischaemic level. It is therefore suggested that the brain facilitates transmission in the Ia disynaptic reciprocal pathway during tonic voluntary dorsiflexion of the foot, but that this facilitation is normally not observed due to a post-activation depression following the peripheral feedback activation during the movement.

Double Chain Ladder

By adding the information of reported count data to a classical triangle of reserving data, we derive a suprisingly simple method for forecasting IBNR and RBNS claims. A simple relationship between development factors allows to involve and then estimate the reporting and payment delay. Bootstrap methods provide prediction errors and make possible the inference about IBNR and RBNS claims, separately.

Time-varying effects in the analysis of customer loyalty

Highlights? We prove that the probability of losing a customer holding multiple contracts in an insurance company fluctuates over time. ? Survival analysis methods with time-varying parameters are used to estimate the probability of customer lapse. ? Customer loyalty after a first cancellation depend on the type of policy retained and the effect of competitors. ? The intensity of the factors explaining customer duration is very high right after a first policy cancellation. ? This approach can be used in a variety of contexts to assess the duration of multiple contracts held by the same customer. Insurance customers usually hold more than one contract with the same insurer. A generalization of classical survival analysis methods is used to examine the risk of losing a customer once an initial insurance policy cancellation has occurred. This method does not assume that the model parameters are fixed over time, but rather that the parameters may fluctuate. Our results suggest that the kind of contracts held by customers and the concurrence of an external competitor strongly influence customer loyalty right after that cancellation, but those factors become much less significant some months later. Our study shows how predictions of the probability of losing a customer can be readjusted and improves the way companies manage business risk.

Prediction of RBNS and IBNR Claims using Claim Amounts and Claim Counts

A model is proposed using the run-off triangle of paid claims and also the numbers of reported claims (in a similar triangular array). These data are usually available, and allow the model proposed to be implemented in a large variety of situations. On the basis of these data, the stochastic model is built from detailed assumptions for individual claims, but then approximated using a compound Poisson framework. The model explicitly takes into account the delay from when a claim is incurred and to when it is reported (the IBNR delay) and the delay from when a claim is reported and to when it is fully paid (the RBNS delay). These two separate sources of delay are estimated separately, unlike most other reserving methods. The results are compared with those of the chain ladder technique.