In light of recent work studying massive functional/longitudinal data, such as the resulting data from the COVID-19 pandemic, we propose a novel functional/longitudinal data model which is a combination of the popular varying coefficient (VC) model and additive model. We call it Semi-VCAM in which the response could be a functional/longitudinal variable, and the explanatory variables could be a mixture of functional/longitudinal and scalar variables. Notably some of the scalar variables could be categorical variables as well. The Semi-VCAM simultaneously allows for both substantial flexibility and the maintaining of one-dimensional rates of convergence. A local linear smoothing with the aid of an initial B spline series approximation is developed to estimate the unknown functional effects in the model. To avoid the subjective choice between the sparse and dense cases of the data, we establish the asymptotic theories of the resultant Pilot Estimation Based Local Linear Estimators (PE-BLLE) on a unified framework of sparse, dense and ultra-dense cases of the data. Moreover, we construct unified consistent tests to justify whether a parsimony submodel is sufficient or not. These test methods also avoid the subjective choice between the sparse, dense and ultra dense cases of the data. Extensive Monte Carlo simulation studies investigating the finite sample performance of the proposed methodologies confirm our asymptotic results. We further illustrate our methodologies via analyzing the COVID-19 data from China and the CD4 data.
cases of different countries are recorded and made available. When a variable is measured or observed at different times, the variable is usually treated as a function of time. As a result, the variable is called a functional variable, the data for the variable are called functional data and the related statistical analysis is called functional data analysis (FDA) ( [38] ). The functional data and corresponding FDA have been successfully applied to explore the interactions and co-movements among a group of temporally evolving subjects. Several monographs by [31] , [32] and [9] provide comprehensive discussions on the methods and applications. More recent work about FDA could refer to [38] .
According to [38] , usually, the functional data could be divided into two cases: sparse and dense. Sparse functional data usually occurs in longitudinal studies where subjects are measured at different time points and the number of measurements for each subject is often bounded away from infinity. Inversely, in the dense functional data the number of measurements of each subject tends towards to infinity. In theory, the difference between sparse (longitudinal) and dense function data is clear. However, due to the limitations of humans, the observations in real data sets could not be infinite and are definitely finite. Therefore, the edge of sparse (longitudinal) and dense function data in practice is vague in some scenarios, especially when the number of measurement of each subject is moderate or different subjects have different numbers of measurements.
In many functional/longitudinal studies, repeated measurements within each subject are possibly correlated with each other, but different subjects can be supposed to be independent. One approach to take intra-subject variation into account is the mixed-effects model [42] , which decomposes regression function into a fixed population mean and a subject-specific random trajectory with zero mean. For sparse and dense functional data, [19] considered a mixed-effects nonparametric regression model absence of covariates, and showed that the asymptotic distributions of kernel estimators are essentially different in these two situations. Therefore, a subjective choice between sparse and dense cases may lead to erroneous conclusion. To evade this problem, they proposed a self-normalized method, which can deal with sparse and dense functional data in a unified framework. Furthermore, [4] generalized the results of [19] to a mixed-effects VCM presence of covariates with sparse or dense functional data. Lately, [47] provided a comprehensive perspective that deals with a general weighing scheme on a unified paltform for all types of sampling plan, including sparse, dense and ultra dense case. Motivated by a monotone relationship between gray matter volume and age in the older population, [5] considered sparse and dense cases on a unified framework under monotone constraint of the mean function. The research work of [47, 5] has focused on the statistical inference about mean function of the underlying process. To the best of our knowledge, there exists no further development about unified inference parallel to [47] for nonparametric regression model presence of covariates, a common case in practice.
In the analysis of longitudinal data, a varying-coefficient models (VCM) enjoying flexibility, parsimony and interpretability, is a widely-used nonparametric regression method. One can refer to [8, 10, 12, 15, 16, 17, 29, 35 ]. An additive model (AM) is another popular nonparametric regression method, which has been studied by [1, 3, 25, 27, 28, 30, 43, 44, 34] . Recently, [13, 14, 46, 48] have investigated a novel nonparametric regression method, named the varying-coefficient additive model (VCAM), which can be viewed as a generalization of the VCM and AM. Let T ij be the observation time when the jth measurement of the ith subject is made, Y ij and X i (T ij ) := X ij be the response and p-covariates for the ith subject at time T ij , respectively. Then {(Y ij , X ij , T ij ) ; i = 1, ..., n; j = 1, ..., m i } constitutes a longitudinal/functional sample from n randomly selected subjects with m i repeated measurements of the ith subject. The VCAM for longitudinal/functional data is proposed by [14] as below
with the abuse of notations. Here ν i (T ij ) is the subject-specific random trajectory at observation time T ij , and {ε ij } are i.i.d. random measurement errors. The multiplicative factors α k (k = 1, ..., p) and β k (k = 1, ..., p) are called to be varying-coefficient component functions and additive component functions, respectively, and α 0 is a trend term. Obviously, the VCAM (1.1) reduces to an AM provided that each α k (k = 0, ..., p) is time-invariant, whilst it becomes a VCM if each β k (k = 1, ..., p) has a simple linear form. Therefore, it can be said that the VCAM is a kind of hybird of an AM and a VCM, enjoying more flexibility, which can greatly decrease the bias of model misspecification. On the other hand, it is hard to address how to choose between an AM and a VCM in practice. The general type of a VCAM provides a data-driven method to decide which model may be more suitable for the real-life data at hand. However, the product forms of α k and β k in (1.1) exclude the discrete covariates from this model. It will vastly limit the scope of applications because categorical variables are often important influence factors in the practical fields. To accommodate both discrete and continuous covariates in regression model, in this paper we consider a mixed-effects semi varying-coefficient additive model (Semi-VCAM) to analyze longitudinal data. Let Z i (T ij ) := Z ij = (1, Z ij,1 , ..., Z ij,q ) τ be a {q + 1}-vector of discrete covariates observed at time T ij , and α 0 (t) = (α 00 (t) , α 01 (t) , ..., α 0q (t)) τ is the vector of varying-coefficient functions for Z that is i.i.d. with Z i , and α 00 denotes the trend function. Then, we generalize the VCAM (1.1) to a Semi-VCAM as below,
where the subject-specific random trajectory
, {ε ij } are random errors such that E (ε ij ) = 0 and E(ε 2 ij ) = 1, and σ (t) is a smooth standard deviation function of process ε (t). Note that (1.2) allows a mixture of functional/longitudinal predictors and scalar covariates, and it reduces to a partial linear additive model (PLAM), if each varying-coefficient function is time-invariant. Compared with the model (1.1) studied in [14] , (1.2) allows categorical covariates and heteroscedasticity as time elapsed. Meanwhile, in this paper we also take into account intra-subject correlation, which was merged into random errors in [14] . Therefore, Semi-VCAM is a more refined nonparametric model than VCAM (1.1) in the analysis of longitudinal data.
As a global smoothing technique, spline method is widely used to fit a smooth nonparametric function because of its merit of cost saving. But it usually has no asymptotic distribution due to absence of decomposition of bias part and variance part, unless the asymptotic bias is smaller of high order than the asymptotic variance. All of the existing research literatures about VCAM are based upon a spline method, [46, 48] provide no asymptotic distributions of estimators, whilst [13, 14] obtain the asymptotic distributions under the condition that the asymptotic bias can be ignored. Alternatively, kernel method is a local smoothing tool, based upon which we can construct the involved asymptotic distribution presence of asymptotic bias, and make statistical inference on certain interested function. Specially, local linear smoothing is popular due to its nice properties, such as design adaption, good boundary performance, and statistical efficiency in an asymptotic minimax sense, see [7] for more details.
In this paper, we build a pilot estimation based local linear estimator (PEBLLE) for varying-coefficient component functions and additive component functions, respectively. The proposed estimation method has wide applicability, including sparse data and dense data, and the data presence of functional/longitudinal covariates and scalar variables. We have shown the consistency of PEBLLE, and as a main contributor of this paper, we con-struct the asymptotic distributions on a unified framework for sparse, dense and ultra dense data. For the convenience of concise presentation, we only consider the same weight to each subject (SUBJ), and our theoretical results can be viewed as a generalization of [47] to nonparametric regression model presence of covariates with SUBJ scheme. Another intriguing question is how to judge a general Semi-VCAM or a submodel is sufficient. To this end, we develop two hypothesis testing to decide whether each varying-coefficient component functions is time-invariant (i.e., a PLAM or especially, an AM if absence of Z covariates), or whether each additive component function has linear form (i.e., a VCM). It has been shown that the proposed testing procedure is consistent on a unified framework of sparse, dense and ultra dense case of data.
In the empirical studies, we consider the new coronavirus disease (COVID-19) breaking out in December 2019, and apply our method to analyze the growth rate of cumulative confirmed (GRCC) cases in China except Hubei Province, Tibet, Macao, Taiwan and Hong Kong. We collect the data from https://github.com/CSSEGISandData/COVID-19, and take sample period from January 22th, 2020 and April 8th, 2020. To model GRCC, four function covariates and one scalar covariate (population size) are chosen. The testing procedures show that a Semi-VCAM is necessary for this dataset. Another example is CD4 data from the Multicenter AIDS Cohort Study (a data set in the R package "timereg"), which has been studied by [15, 45] . In this model, smoke status (1 for smoker and 0 for nonsmoker) is included. Employing Semi-VCAM, the testing procedure shows a VCM is sufficient, which verifies the rationality of the research results in [15] .
The rest of this paper is organized as follows. Section 2 proposes a pilot estimation based local linear smoothing method and Section 3 presents a series of the asymptotic theories. In Section 4, we propose a testing procedure to justify whether a VCM or a PLAM is sufficient or not, and show its asymptotic properties. Section 5 speaks about the implementation of the proposed method. Extensive simulation studies investing the finite-sample performance and real data applications illustrating our methodologies are considered in Section 6. Brief remarks are concluded in Section 7. The requirements for validity of the asymptotic theories are presented in the Appendix, and the main proofs are relegated to the Supplementary Material.
We assume that observation time {T ij } are i.i.d. copies of T , which has a density function f T with a bounded support, say [a, b]. The vector of covariates X i = (X i1 , ..., X im i ) τ for the i-th subject is randomly drawn from a p-dimension stochastic process X(T ), of which the k-th element X k (T ) has a marginal density function f X k with support S k . To identify the trend term and product terms in model (1.2), we impose the conditions E [α k (T )] = 1 and E [β k (X k )] = 0 (k = 1, ..., p), a similar practice with [48, 14] .
In this section, we develop pilot estimation based local linear estimators (PEBLLEs) for α k and β k . Suppose that β k 's are known, then Semi-VCAM (1.2) become a VCM, and the LLE of α k 's are easily obtained. Let
We solve the optimization problem as below
where k h (·) = k (·/h) /h for certain kernel function k. Then the LLE of varying-coefficient component functions are given byα 0l (t) =â 0l for l = 0, ..., q andα k (t) =â k for k = 1, ..., p.
On the other hand, if α k 's are known, then Semi-VCAM reduces to an AM. Suppose that we have got estimation of additive component functions except β k , denoted asβ l for l = k, and consider the following minimum problem
However, both α k and β k are unknown, implying the above-mentioned estimation methods are infeasible. To this end, we propose pilot estimations of additive component functions. Similar to [14] , we view multiplicative term α k (t) β k (x) as a general bivariate function, say g k (t, x), and estimate it using tensor B-spline method. Specifically, for any given t and x, the tensor product is defined as
where ⊗ means the Kronecker product of matrices or vectors, and b C (t) and B k,A (x) denote the B-spline basis approximating α k (t) and β k (x), respectively.
Then, we approximate α 0l (t) ≈ γ τ 0l b C (t) for l = 0, ..., q, and g k (t,
.., p. Solving the following optimization problem
Hence, a pilot estimator of additive component function β k can be given by
where N = n i=1 m i is total observation, and subscript 'P' means pilot estimator. Now, we can define the PEBLLEs of varying-coefficient component functions and additive component functions.
• Substituting the pilot estimatorsβ k,P (k = 1, ..., p) into the objective function Q 1 , we obtain the PEBLLE of α 0l for l = 0, ..., q and α k for k = 1, ..., p, and still denote them asα 0l andα k , respectively. • In the objective function Q 2 , we take the PEBLLEs of varying-coefficient component functions as their pilot estimations, andβ l,P (l = k) as the pilot estimators of additive component functions, and yield the PE-BLLE of β k , still write asβ k .
Remark 1. Compared to the spline-based estimators of [14] , the PE-BLLE can provide asymptotic distribution with the specific expression of asymptotic bias, and make inference on the confidence interval of component functions. Meanwhile, our estimation methodologies adapt to both sparse and dense longitudinal/functional data, and have wide application in the real word.
3. Asymptotic Results. In this section, we will present the asymptotic distribution and convergence rate of PEBLLE on a unified platform for different sampling plans.
1 m i −1 be the harmonic mean of {m 1 , ..., m n }, and denote the interior knots number of B-spline basis b C (t) and B k,A (x) (k = 1, ..., p) as K C and K A , respectively. Then, based upon the result of Proposition 1 presented in Supplement A, Theorem 3.1 shows the uniform convergence rates of PEBLLEs of varying-coefficient component functions.
where l = 0, ..., q and k = 1, ..., p.
Remark 2. From Theorem 3.1, we notice that the variance term obtains a nonparametric rate of convergence log n/(nN H h C ) provided that Remark 3. Similar to [47] , we split data into sparse, dense or ultra dense according to the ratioN H /n 1 2r tends to 0, a nonzero constant or ∞ as n → ∞. In fact, we give a more general method of partitioning data in the sense that the same split with [47] is used if r = 2.
Let α (t) = {α 00 (t) , α 01 (t) , ..., α 0q (t) , α 1 (t) ..., α p (t)} τ , andα (t) the PEBLLE of α (t). Furthermore, we introduced the following symbols:
Theorem 3.2 presents a unified asymptotic normality ofα (t), which can be applied to sparse, dense and ultra dense cases of the data.
holds. Then, for any an interior t in (a, b), we obtain the asymptotic distribution ofα(t) as below:
According to the method of partitioning data defined in Remark 3 and (3.1), Corollary 1 lists the asymptotic distributions for sparse, dense and ultra dense cases of the data as follows.
Corollary 1. Suppose that the conditions of Theorem 3.2 hold and t is a fixed interior point on the interval (a, b).
and v 2l (t). Then, the naive consistent estimators of asymptotic bias ρ 1 (t) and asymptotic variance Γ C (t) are given byρ 1 
where z 1−α/2 is the 1−α/2 standard normal quantile, the subscript k denotes the k-th element of involved vector, and the subscript (k, k) means the k-th diagonal element of a given matrix. Note that (3.5) is a unified confidence interval suitable for sparse, dense and ultra dense cases of the data.
In this subsection, we focus on the asymptotic results of PEBLLE of additive component functions. Theorem 3.3 gives the uniform rates of convergence of β k .
Theorem 3.4 presents the asymptotic normality ofβ k on a unified formwork for different types of data.
hold. Then, for any an interior x in S k , we have
As a corollary, we get different asymptotic results for sparse, dense and ultra dense data.
Corollary 2. Suppose that the conditions of Theorem 3.4 hold and x is a fixed interior point in S k .
Letμ k ,f X k (x) andψ k,j , j = 1, 2 be consistent estimators of µ k , f X k (x) and ψ k,j . Then, the asymptotic variance Γ A (x) can be consistently estimated byΓ
which gives a (1 − α)% pointwise confidence interval of β k in a unified forms for sparse, dense and ultra dense data. That is,
For the sake of parsimony, it is essential to test time-varying property of varying-coefficient component functions and to test linearity of additive component functions.
In this subsection, we propose a consistent testing to judge whether the varying-coefficient component functions are really time-varying or not. It is a problem of model selection between a general Semi-VCAM and a submodel PLAM or an AM in the practical applications.
We denote δ ij = ν i (T ij ) + ε ij in Semi-VCAM (1.2), and consider a mixed-
The time-varying testing postulates m as
under null hypothesis H 0,C , where a = (a τ 0 , a 1 , ..., a k ) τ is a unknown constant vector, and β (x) = (β 1 (x 1 ), ..., β p (x p )) τ . Whilst under alternative hypothesis H 1,C , m is the regression function of Semi-VCAM (1.2), denoted as g (t, z, x; α (t) , β (x)). Then, the interested hypothesis is given as below
Under H 0,C , we replace β k (x) with PEBLLEβ k (x), and obtain the parametric estimator of vector a as follows
For the i-th subject and the j-th subject, we introduce the weight ma-
, and we propose a testing statistic based upon the quadratic form of residuals as follows
whereê i = (ê i1 , ...,ê im i ) τ and |H| = h C h p A . Furthermore, we assumes additional conditions as follows.
and Ω F is bounded away from zero and infinity, where S i is the analogue ofŜ i afterβ k being replaced by β k , and · F is the Frobenius norm of the involved matrix. under H 0,C , wherê
is a consistent estimator of the asymptotic variance of Let h 0 (t, z, x; α(t)) = z τ α 0 (t) + p k=1 α k (t)x k . It is expected to test H 0,A : m (t, z, x) = h 0 (t, z, x; α (t)) a.s. ↔ H 1,A : m (t, z, x) = g (t, z, x; α (t) , β (x)) a.s. (4.4) Denoteα (t) as the LLE of α (t) under null hypothesis H 0,A . Then, the testing statistics is given by
). The asymptotic distributions ofÎ n under H 0,A and H 1,A are presented in the following two theorems. 
In this section, we address the practical issues that arise in the newly-proposed methodologies.
• B-spline method of pilot estimation As a common practice in spline smoothing, we predetermine the order of the B-spline functions and then select optimal interior knots number through BIC criterion BIC (K C , K A ) = log (RSS) + N log n/n,
and N = (q + 1) J C + pJ C J A , with J C and J A being the dimension of Bspline basis space b C (t) and B k,A (x k ). Then the optimal interior knots number is given by (K C ,K A ) = arg min BIC (K C , K A ).
In local linear smoothing, we use Epanechnikov kernel function k (u) = 0.75 1 − u 2 I |u|≤1 , and select the optimal bandwidths using "leave-one-out" cross-validation procedure suggested by [33] . Define the subject-based crossvalidation (CV) criterion as below
where the subscript "−i" represents the estimator using the data with all repeated measurements except the ith subject. The optimal bandwidth is the unique minimizer of CV(h C , h A ).
In simulation studies, we also can use the average squared error (ASE) as follows
Similar to Remark 2.3 of [40] , it is not difficult to show that the CV bandwidths approximately minimize ASE.
6. Numerical Studies.
In this subsection, we consider simulation examples to investigate the finite-sample performance of the proposed estimation method in Section 2 and the testing procedure in Section 4. 
The response Y ij is generated by a mixed-effects Semi-VCAM as below
for i = 1, ..., n and j = 1, ..., m, where the measurement error ε ij are i.i.d from N (0, 1), and the subject-specific random trajectory ν i (T ij ) = η i1 + √ 2η i2 sin (2πT ij ) + √ 2η i3 cos (2πT ij ) with η ij ∼ N (0, w j ) for j = 1, 2, 3 and (w 1 , w 2 , w 3 ) = (0.6, 0.2, 0.2). The univariate smooth component functions are given by α 00 (t) = 6t, α 01 (t) = 2.5 cos (2πt), α 1 (t) = t(1−t) We select 20 equally-spaced points on the range of T ij and X ij , and define the mean prediction integrated squared error (MPISE) based on Q replications,
wheref q is the PEBLLE of the estimated function f in the q-th replication. Under different combinations of n and m, based upon Q = 300 Monte Carlo replications, Table 1 gives the MPISEs of PEBLLE of component functions, and the standard deviation is shown in parentheses. We also list the optimal bandwidths according to (5.1). The result exhibits a good finite-sample performance whatever the data is sparse or dense. It is also found that MPISEs decrease markedly as the total observations increase. We also investigate the performance of asymptotic distribution given in (1) the AECPs of unified method (bold tags in tables) are superior to the other three methods, whatever the data is sparse, dense or ultra dense; (2) the AECPs of sparse method decrease as m grows, and they are inferior to the unified method even for sparse data; (3) the AECPs of dense and ultra dense method increase as m grows, and they are comparable to that of the unified method.
Example 2. Now we investigate the performance of hypothesis testing constructed in Section 4. To this end, we consider the following two DGP:
• DGP I: In this case, we test the time-varying property of varyingcoefficient component functions, that is to decide whether a PLAM is sufficient. We take the same settings with Example 1 for T ij , X ij , Z ij , ε ij , ν i and β 1 (x). The time-varying testing of conditional mean function m (t, z, x) is as follows where g 0 (z, x) = 6 + 2.5z + β 1 (x) and
with θ = 0.2, 0.4, 0.6, 0.8, 1.0. • DGP II: Here we consider the linearity testing of additive component functions to judge whether a VCM is sufficient. Let T ij , Z ij , α 00 (t) and α 01 (t) be given in Example 1, α 1 (t) = sin (πt)/ 1 0 sin (πt)dt, and N (0, 1) . The interested hypothesis is given by
where h 0 (t, z, x) = α 00 (t) + α 01 (t) z + α 1 (t) x and h 1 (t, z, x) = h 0 (t, z, x) + 1.5θα 1 (t) sin (πx) with θ = 0.2, 0.4, 0.6, 0.8, 1.0.
We consider different combinations of n = 30, 50, 100 and m = 5, 10, 30, 60, 100, and generate Q = 300 Monte Carlo replications and B = 300 bootstrap samples for each simulated data set. Under 5% and 10% significance levels, based upon bootstrap critical value, Table 4 and 5 present power of hypothesis (6.1) and (6.2) for different deviation parameters θ ranging from 0 to 1 with the span of 0.2, respectively. The results show that the proposed testing procedure all performs well for sparse, dense and dense data. In fact, the power for θ = 0 is size of hypothesis, which is close to the theoretical significance level 0.05 or 0.1. As expected, the power increases to one as θ ascends whatever significance levels and sampling plans. Moreover, Figures  2 and 3 plot the rejection rates of testing (6.1) and (6.2) at the 5% and 10% significance levels for some combinations of n and m, respectively.
Example 3. Now we apply our method to the new coronavirus disease (COVID-19) mentioned in Section 1. We collected the daily cumulative confirmed cases (Z i,t ) and the daily cumulative cured cases from https://github.com/CSSEGISandData/COVID-19, the daily movement population from Wuhan to other provinces (https://qianxi.baidu.com/), the maximum daily temperature (http://www.weather.com.cn) and the population data (https://zh.wikipedia.org/wiki/).
The response variable GRCC, denoted by Y i,t , is measured by log (Z i,t ) − log (Z i,t−1 ), which is presented in Figure 4 (a) for 29 provinces in China from January 23th to April 8th. We notice that the big values of GRCC (above 0.5) mainly concentrate on the period from January 23th to February 3th. It is a strong evidence that the intervention policy of China's government plays a positive role in controlling the spread of Coronavirus disease.
To explore the influence factor of GRCC, we used five covariates: X 1,it being the movement population from Wuhan (MPFW), which is measured by the proportion of the population moving from Wuhan to the ith province out of moving out population at day t − 14; X 2,it the daily cumulative cured cases (CUCC) at day t − 1; X 3,it the daily cumulative confirmed cases (CFCC) at day t − 1; X 4,it the maximum daily temperature at day t, and X 5,i the population of ith province.
We normalize the covariate X 1,it , and make the logarithm transformation for X k,it , k = 2, 3 and X 5,i . Based on 500 bootstrap sampling, we do the time-varying testing (4.1) and linearity testing (4.4), obtaining the p values 0.028 and 0.038, respectively. Therefore, we reject the AM and VCM at significant level 0.05, and adopt the general model as below:
where i = 1, ..., 29, t = 1, ..., T with T = 77. Figure 4 gives the PEBLLE of component functions, and 95% pointwise confidence bands according to (3.5) and (3.10). From Figure 4 , we conclude that the trend term α 0 , varying-coefficient function α 1 for MPFW, α 3 for CCFC and α 4 for MDT have similar properties, i.e., they drop rapidly until about February 29th, and then maintain on the level close to zero; α 2 for CUCC decreases until about February 22th, and increases until about March 9th, and thereafter levels near zero; α 5 for POP decreases slowly until about February 9th, then increases until about February 29th, and decreases thereafter.
For the medium values of the normalized MPFW, the effect increases as MPFW grows, and some fluctuations appears for the large value (above 2), since large MPFW usually takes place in the early stage and the period of work resumption. The influence of CUCC increases as it grows, and the rate of increases become slower above 2; while the effect of CCFC ascends as it increases, and levels out above 3. The effect of MDT drops under 0℃, and increases until 10℃, and almost no influence between 10℃and 20℃, then ascends rapidly above 20℃. The trend of effect of POP grows as the population size ascends, especially when log-POP is larger than 7.5.
Example 4. We revisit a CD4 data from the Multicenter AIDS Cohort Study, which contains 1817 observations from 283 homosexual men infected with HIV between 1984 and 1991. [4, 15] have analyzed this data set using a VCM. Now, we apply our method to this dataset. The response variable Y ij is the i-th subject's CD4 percentage at time T ij . Following the covariates of [15] , we let X 1i be the i-th subject's smoke status, a dichotomous variable, X 2i the i-th subject's centred age, and X 3i the i-th subject's centred preinfection CD4 percentage. The relationship between response and covariates are modeled by a Semi-VCAM as below
where the covariates are all time-invariant. Based on 500 bootstrap sampling, we do the time-varying testing (4.1) and the linearity testing (4.4), obtaining the p values 0.028 and 0.457, respectively. That means, at significant level 0.05, VCM is a reasonable choice, which verifies that the model used in [15] is appropriate.
7. Concluding Remarks. In this paper, we have considered a Semi-VCAM for the functional/longitudinal data with different sampling plan. The Semi-VCAM is an extension of the existing VCAM. We have developed a pilot estimation based local linear estimation for the Semi-VCAM and have presented asymptotic distribution on a unified platform for sparse, dense and ultra dense cases of the data. The virtue of unified asymptotic results is to help us avoid deciding the types of data in advance, which is a subjective choice and may lead to wrong conclusions. From the viewpoint of model parsimony, we also have developed consistent testing procedures to justify whether a VCM or PLAM, especially an AM is sufficient for the real-life data. These test methods also avoid the subjective choice between the sparse, dense and ultra dense cases of the data.
Our model and inference methods may be extended in various directions. We close the paper by outlining some of them. In many application areas, data may be collected on a count or binary response. For example, daily death toll, suspected and confirmed cases of COVID-19. As a result, it is useful to extend our proposed model and inference to the generalized Semi-VCAM to accommodate the discrete functional/longitudinal responses. Data in the form of samples of densities or distributions are increasingly encountered in practice and same as [11] there is a need for flexible regression models that accommodate random densities as responses. We believe our proposed model could also be used to model the data in which the responses are random densities. In addition, due to the fact that the proposed test method in our paper is based on the local smoothing, it may suffer the curse of dimensionality, struggle to maintain the significance level and lose its power to an extent as the dimension of explanatory variables increases. Same as [20] and [22] , we may use projection technique, or bridging between local smoothing and global smoothing methods to avoid this. Due to the complication of our model, extending the methods in [20] and [22] to our scenario is not simple. 